effects of internet addiction essay

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Internet Addiction Effect on Quality of Life: A Systematic Review and Meta-Analysis

Farzaneh noroozi.

1 Department of Health Promotion, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran

Soheil Hassanipour

2 Cardiovascular Diseases Research Center, Department of Cardiology, Heshmat Hospital, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran

Fatemeh Eftekharian

3 Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran

Kumars Eisapareh

Mohammad hossein kaveh.

4 Research Center for Health Sciences, Institute of Health, Department of Health Promotion, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran

Associated Data

The data used to support the study are available from the corresponding author upon request.

Due to the use of different methodologies, tools, and measurements, the positive or negative impact of Internet use on human life quality is accompanied by a series of ambiguities and uncertainties. Therefore, in this study, a systematic review and meta-analysis are conducted regarding the effect of Internet addiction on the quality of life.

A systematic search of resources was conducted to investigate the effect of Internet addiction on the quality of life. The databases of PubMed, Cochrane Library, Scopus, Web of Science, Embase, and Science Direct were searched from January 1980 to July 2020. The articles were screened by two researchers in multiple levels in terms of the title, abstract, and full-text; then, final studies that met the inclusion criteria were retrieved and included in the study.

After searching the previously mentioned international databases, 3863 papers were found, 18 of which we included in the final analysis. Surveys indicated that people who had a high Internet addiction received lower scores of quality of life than those who were normal Internet users (OR = 2.45, 95% CI; 2.31–2.61, p < 0.001; I 2  = 85.23%, p < 0.001). Furthermore, There was a negative significant relationship between Internet addiction and quality of life in the psychological (OR = 0.56, 95% CI: 0.32–0.99, p =0.04, I 2  = 97.47%, p < 0.001), physical (OR = 0.58, 95% CI: 0.39–0.86, p =0.007, I 2  = 95.29%, p =0.001), and overall quality of life score (OR = 0.39, 95% CI: 0.27–0.55, p < 0.001, I 2  = 0.0%, p =0.746).

These findings illustrate that Internet addiction should be regarded as a major health concern and incorporated into health education and intervention initiatives.

1. Introduction

Among the different media types, the Internet is a recent achievement of mankind, a highly reachable global medium with an advanced modern communication technology capable of providing access to a wide range of information sources [ 1 , 2 ].

Although the Internet and its technologies have provided valuable opportunities in scientific, communicative, and economic aspects for human societies, its inappropriate and extreme application, mostly for recreational purposes, is a serious threat to the health and well-being of the human population, especially young people [ 3 ]. According to studies, the increasing demand for Internet technology is associated with major health, psychological, and social problems, overwhelming mental health, interactions, and communications. Researchers also believe that excessive use of the Internet and social networks can indicate stress, anxiety, and depression; indeed, the excessive use of these networks is a way to reduce negative emotions [ 4 ].

The Internet affects various dimensions of lifestyle, social interaction, and occupational performance in both positive and negative ways. As to its positive effects, people can solve most of their daily problems via the Internet. In terms of developing interpersonal relationships, it goes beyond the geographical boundaries. Further, it has become an important part of everyday lives by helping exchange information and personal or professional experience, carry out economic/commercial activities, reduce transportation costs and problems, and develop business and marketing activities [ 5 ].

Negative effects have also been reported as real physical communications are decreasing compared with online communications due to the power of new technologies in the development and transformation of social communications, leading to weaker social relationships in the real world [ 6 ]. Overall, the Internet and social networks are not only changing human relationships and interactive patterns but also create intense interactions and influence individual life [ 7 ].

Internet addiction (IA) is an extreme form of this phenomenon, an inability to avoid using the Internet that has adverse effects on various life aspects (e.g., interpersonal relationships and physical health) [ 8 ]. It is considered a disorder in the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) [ 8 , 9 ]. Estimation of the IA prevalence varies widely across countries (1.5% to 10.8%) [ 4 , 10 , 11 ]. Based on a meta-analysis result, its prevalence is 6% in 31 countries; the highest prevalence is 10.9% in the Middle East, and the lowest rate of 2.6% belongs to the north and west of Europe [ 8 ].

The studies of the IA show the reduction of life satisfaction in terms of family, friends, school, and living environment. References [ 12 – 14 ] have shown negative effects of IA on physical health aspects. As reported, the use of social networks causes insomnia, physical inactivity, and eye problems, as well as depression, social phobia, and hyperactivity disorders, in most users [ 12 , 15 ]. Based on a meta-analysis by Ho et al., IA is significantly associated with alcohol abuse, attention deficit, hyperactivity, depression, and anxiety [ 16 ].

As mentioned, the Internet has a great impact on various aspects of health. According to the World Health Organization (WHO), health is defined as a complete state of physical, mental, and social well-being [ 17 ]. Quality of life (QOL) is a comprehensive measure of health outcomes [ 18 ]. It is a multidimensional concept that includes understanding mental and objective conditions of individuals' life in their sociocultural and economic environment [ 19 ]. The effect of excessive Internet use on health can be found by examining the impact of IA on QOL.

Several studies have indicated that IA reduces QOL [ 20 ]. On the contrary, some other studies have shown an insignificant association between the use of the Internet and social networks with QOL [ 21 , 22 ]. For example, Ko et al. claim no relationship between QOL and moderate or intense Internet use [ 23 ]. A review study by Veenhoven, who examined IA and its derivatives, showed that the Internet increases QOL, and if the IA really exists, it can affect a relatively small percentage of an online population [ 24 ]. According to another review study, there is a positive relationship between using the Internet and computer and the QOL of the elderly [ 25 ]. Tran et al. have shown that the Internet can help people obtain a higher perceived QOL by promoting their work, education, and communication [ 26 ]. A cross-sectional study of college students found that the quality of life in daily users of social networking sites was higher than that of nondaily users [ 27 ].

On the other hand, a more in-depth study on types of applied programs on the Internet by individuals indicates the impact of a particular program on the individuals' mental well-being. In other words, spending time on programs involved with photo and video sharing is associated with higher levels of depression and anxiety; in contrast, using programs involved with book reading reduces depression and anxiety, thus increasing levels of mental well-being [ 28 ]. Researchers have also reported that people who spend much time online have lower perceived QOL due to the lack of long-term sleep, deteriorated physical health, difficulties in concentrating on work, and reduced intimacy with family members [ 29 , 30 ].

The association between the Internet and the quality of human life is accompanied by a series of ambiguities and uncertainties due to the wide range of its potential positive and negative effects. Possible reasons may be different methodologies and tools, leading to differences in the measurement of Internet use rates. The selection of a specific and agreed form of inappropriate use, namely, the IA, as an independent variable and the definition and measurement based on well-known tools and standards of QOL as a consequence can probably result in precise findings on their relationship. According to the previously mentioned considerations, a systematic review and meta-analysis are conducted on the impact of IA on QOL.

2. Materials and Methods

The present study was a systematic review and meta-analysis. A systematic search of resources was conducted by a librarian (L.E) to investigate whether IA affects the QOL (condition) of people (population) across the world (context).

The research method was based on the PRISMA checklist [ 31 ].

2.1. Data Sources and Search Strategy

The Web of Science, Scopus, Cochrane Library, Embase, Science Direct, and PubMed databases from Jan 1980 to Jul 2020 were searched to find English articles. Also, SID and Magiran databases were searched for Persian studies. The grey literature and ongoing studies were searched in OpenGrey and Google Scholar; further, ProQuest was searched for thesis, dissertations, and studies presented at conferences.

The search was performed using MESH and free keywords. The keywords selected for the search were “Internet addiction” and “quality of life.” After determining relevant keywords, searches were done on databases using associated keywords with “AND” and “OR” operators combined together to determine relevant terms and synonyms. Search strategy included the following keywords: “compulsive Internet,” “computer addict,” “cyber addict,” “excessive Internet use,” “Internet addict,” “Internet dependent,” “Internet disorder,” “Net addict,” “online addict,” “quality of life,” “life quality,” and “health related quality of life.” The PubMed advanced mesh search features used for example were: (((((((((((((“quality of life” [MeSH Terms]) OR (“value of life” [Title/Abstract])) AND (impact [Title/Abstract])) AND (“Internet addiction” [Title/Abstract])) OR (“problematic Internet use” [Title/Abstract]) OR (“online gaming addiction” [Title/Abstract])) OR (“game addiction” [Title/Abstract])) OR (“excessive Internet use” [Title/Abstract])) OR (“social media addiction” [Title/Abstract])) OR (“Internet dependency” [Title/Abstract])) OR (“pathological Internet use” [Title/Abstract])) OR (“computer addiction” [Title/Abstract])) OR (“social networking addiction” [Title/Abstract])) OR (“pornography addiction” [Title/Abstract])). The complete search strategy of other databases is in Supplementary File 1 .

The collected information entered EndNote, X7 (Thomson Reuters, Carlsbad, CA, USA), and duplicate papers were automatically deleted.

All cross-sectional, case-control, and cohort studies that examined the relationship between IA and QOL were searched.

2.2. Inclusion Criteria

• The study type had to be observational (cross-sectional, case-control, and cohort).
• The study was required to investigate the relation between IA and quality of life.
• The correlation level ( r ) between IA and quality of life had to be presented, or information based on which the correlation could be computed was required to be given.
• Papers had to be in English (due to the lack of translators for other languages) and Persian.

2.3. Exclusion Criteria

• The authors did not provide further information upon request, including the correlation level ( r ) between IA and QOL.
• Articles that had full texts written in non-English or non-Persian in spite of having abstracts in English or Persian were excluded.
• The study type was nonobservational (qualitative and interventional studies).

2.4. Study Selection

The selected articles were screened in multiple levels based on the title, abstract, and full-text; then, final studies that met the inclusion criteria were retrieved and included in the study. The initial search was conducted by two people. If there was unmatching between them, the team's supervisor (corresponding author) announced the final comment on that paper.

2.5. Articles' Quality Assessment

The STROBE checklist was used to check and control the quality of papers. This tool consists of 22 questions classified into “yes, no, and unclear.” It aims to assess the methodological quality of studies and strategies to identify bias in designs, implementations, and analyses in studies. During the evaluation process, papers with less than 50% of the inclusion criteria were excluded from the study [ 32 ].

2.6. Data Extraction and Quality Assessment

The information extracted from the articles was entered in the extraction form. Extracted data included: first author, year of publication, study name, country of study, sample size, sample characterization, age mean (SD), and study instrument.

2.7. Statistical Analysis

The heterogeneity between studies was examined by Cochran's test (with a significant level less than 0.1) and its composition using I 2 statistics (with a value greater than 50%). A random-effect model was used in the presence of heterogeneity, while a fixed-effect model was used in its absence. The odds ratio (OR) index, obtained from the comprehensive meta-analysis (CMA) software was used for comparing meta-analysis results. All analyses were done using the statistical CMA 2 software.

3.1. Search Results

Studies were reviewed and selected in three stages. At the first stage, 3863 papers from bases using keywords were retrieved and transferred to the reference management software (Endnote). Titles of papers were reviewed, and 1651 repetitive and 2178 irrelevant papers (to the main subject of research) were deleted. At the second stage, 34 papers associated with the main purpose of the project were selected by studying 2212 abstracts of the remaining papers. At the third stage, 14 studies were included in the final review by investigating the full text of 34 papers and considering inclusion criteria. The papers excluded at this stage were those with English abstract but non-English full text (two articles) and qualitative and interventional methodologies (9 articles), and not receiving the correlation level ( r ) between IA and QOL after communicating with authors (five articles). Finally, the results were evaluated using 18 papers eligible for inclusion in the study. Figure 1 shows the process of retrieving and selecting articles.

Flowchart of the included studies in systematic review.

3.2. Articles' Quality Assessment

All studies met more than 50% of the inclusion criteria (medium or high quality) and no studies were excluded during the evaluation process.

3.3. Characteristics of Included Studies

Table 1 presents the specifications of the articles investigated [ 33 ].

Data extraction results from studies.

3.4. Statistical Analysis

The meta-analysis results were divided into several sections in the present study: first, the comparison of QOL of ordinary people with IA people based on overall scores of QOL and each of its dimensions; second, the analysis of the relationship between the severity of IA and QOL based on r index and calculated OR index.

Due to the high heterogeneity of the analysis, the relationship between the severity of Internet addiction and each dimension of the quality of life, a power analysis was performed. The high power of the analysis for each dimension of the quality of life showed that the results of the study were not affected by heterogeneity.

3.5. Comparing the Quality of Life of Ordinary People with Internet Addicts

Four studies examined the overall scores of both groups. Based on the results, people with a high IA (779 participants) received lower scores of QOL than those with normal Internet use (2589 participants) (95% CI: 2.31–2.61; I 2  = 85.23%, p < 0.001).

Four studies examined other QOL dimensions. Based on the obtained results, people with severe IA received lower QOL scores than those with normal Internet use in terms of the environmental (95% CI: 1.65–2.08; I 2  = 22.45%, p =0.276), physical (95% CI: 2.44–2.93; I 2  = 0.0%, p =0.962), psychological (95% CI: 2.71–3.57; I 2  = 38.32%, p =0.182), and social dimensions (95% CI: 1.63–2.95; I 2  = 86.31%, p < 0.001). Figure 2 shows results of the Forest plot for comparison of the QOL of ordinary people with IA ( Figure 2 ).

Comparing the quality of life of ordinary people with that of internet addicts.

3.6. The Relationship between the Severity of Internet Addiction and Quality of Life

The research results indicated that IA is associated with a decrease in QOL. There was a negative significant relationship between the severity of IA and QOL in psychological (95% CI: 0.32–0.99; I 2  = 97.47%, p < 0.001) ( Figure 3 ), physical (95% CI: 0.39–0.86; I 2  = 95.29%, p =0.001) ( Figure 4 ), and overall QOL (95% CI: 0.27–0.55; I 2  = 92.7%, p < 0.001) ( Figure 5 ); however, no statistical significant reduction was observed in environmental (95% CI: 0.50–1.06; I 2  = 93.89%, p < 0.001) ( Figure 6 ) and social dimension (95% CI: 0.45–1.24; I 2  = 96.63%, p < 0.001) ( Figure 7 ).

The relationship between the severity of internet addiction and the quality of life in the psychological dimension.

The relationship between the severity of internet addiction and the quality of life in the physical dimension.

The relationship between the severity of internet addiction and the overall quality of life score.

The relationship between the severity of internet addiction and the quality of life in the environmental dimension.

The relationship between the severity of internet addiction and the quality of life in the social dimension.

Based on the results of the Egger ( p =(0/601)) and Begg test ( p =(0/945)), no publication bias was observed among studies due to the symmetry of the funnel plot ( Figure 8 ).

Funnel plot for assessing possible publication bias.

4. Discussion

Despite the increasing volume of research on the relationship between IA and QOL, no systematic review or meta-analysis has been conducted to summarize the findings to the best of the authors' knowledge. More specifically, the first study assessing IA and QOL has been published in 2013 [ 34 ]. Accordingly, the association between IA and sleep has been studied over the last seven years, and the cumulative evidence requires to be summarized. The present review is the first meta-analysis that uses empirical evidence from the past seven years to understand the association between IA and QOL. By a rigorous selection method using PRISMA guidelines, 18 studies with 11,097 participants were included in the present meta-analysis.

The high power of analysis for each dimension of quality of life showed that the results of the study were not affected by heterogeneity; one of the reasons could be the high number of samples in the study.

Meta-analysis results show differences in QOL based on Internet usage. As the results of four studies show that people with a high IA receive lower scores of QOL than those with normal Internet use (OR: 2.45, p < 0.001). This result was consistent with those of other studies in the field [ 35 – 38 ]; these results suggest that, in comparative studies, even after controlling some background variables affecting QOL, there are still significant independent correlations between IA and all aspects of QOL.

According to the obtained results of the meta-analysis (11studies), IA is associated with a decline in overall QOL (OR: 0.39; p < 0.001). This result, except in one [ 39 ] case, is consistent with the results of other studies included in the analysis [ 36 , 40 – 48 ]. In these studies, an Indian study had the smallest sample size which was 60 [ 39 ], and a Filipino study had the largest sample size, which was 1447 [ 40 ]. The studies are also conducted across 11 countries mostly located in Asia ( n  = 11), followed by Europe ( n  = 5) and the USA ( n  = 2). Although the meta-analysis results in the present review are primarily derived from Asian populations, based on the Egger ( t : 0.539, p : 0.601) and Begg test ( z : 0.137, p : 0.190), no publication bias is observed among them. Additionally, with moderate- and high-quality studies using the STROBE checklist and standard measurement tools, the methodological concerns might have minimal impacts on the present findings.

The research results indicate a significant negative relationship between IA and QOL in the psychological (OR = 0.56, p =0.04) and physical dimensions (OR = 0.58, p =0.007). Different and sometimes contradictory results are reported in studies on the impact of IA on the QOL dimensions. For instance, in two studies by Solati et al. in Iran [ 44 ] and Kelley and Gruber in USA [ 34 ], IA decreased the QOL physical effect. Further, in a study by Lu et al. in China, IA reduced QOL in terms of physical, psychological, and environmental aspects [ 35 ]. Fatehi et al. [ 36 ] in Iran showed that IA decreased the QOL in physical, psychological, and social dimensions [ 36 ]. The results of three studies in Taiwan [ 37 ], China [ 49 ], and the USA [ 38 ] indicated that IA decreased the QOL in physical, social, psychological, and environmental aspects. In addition to a small number of cross-sectional studies, which make the comparison and deduction of causal relationships difficult, differences in contexts and ignorance of the underlying factors affecting the QOL dimensions (such as unemployment, chronic diseases, mental/psychological disorders (depression, negative feelings, and stress)) can be considered as reasons for the contradiction between results on the Internet impact on QOL dimensions [ 45 , 50 , 51 ].

On the other hand, a study conducted in Taiwan shows three specific IA manifestations (compulsive, interpersonal, health, and time management problems) to reduce the physical dimensions of QOL among college students. A possible explanation is that participants with higher compulsivity may have impaired control over Internet use, thereby developing the other two types of IA problems manifested through unhealthy lifestyles, such as poor diet and sleep deprivation, leading to lower physical QOL. Also, compulsivity concerning Internet use may cause poor mental health (depression, loneliness, anxiety, and stress), harming psychological HRQOL [ 37 ].

A longitudinal study in Hong Kong show that time management problem (staying online longer than originally intended) is considered the most common among the participants during the study period [ 52 ]. Such findings show the need for the implementation of IA intervention programs (time management, self-regulation, and self-efficacy) to prevent the deterioration of IA-related physical HRQOL.

4.1. Strengths and Limitations

Despite the increasing influence of the Internet in daily life, in the last eight years, no meta-analysis study has been conducted to investigate the effect of IA on QOL, and this study is the first study in this period.

The quality of the studies has been determined according to the information in the articles, and it is possible that the studies were of higher quality but did not provide all the information and as a result were in the group of medium-quality articles.

The study protocol was not registered before the start for this review and is considered as one of the limitations of the study because there is a concern that it may add to the possible bias over time.

5. Conclusion

According to the present review results, the Internet negatively affects overall QOL, physically and psychologically. Since the Internet meets the needs of information, entertainment, and social interactions, its use is an integral part of everyday human life (both work and leisure). Internet use can also trigger a compulsive need in a minority of individuals. These findings show that IA should be regarded as a major health concern and incorporated into health education and intervention initiatives. Also, further studies are suggested, in particular with a cohort and empirical design in different societies, using standardized methodologies and analytical reports that facilitate the comparison.

Acknowledgments

The authors acknowledge all the participants who were involved directly and indirectly in the study and provided professional, technical, and nontechnical support.

Abbreviations

Data availability, conflicts of interest.

There are no conflicts of interest regarding the publication of this study.

Supplementary Materials

The complete search strategy of other databases is provided as Supplementary File 1.

Home — Essay Samples — Information Science and Technology — Internet — The Impact of Internet Addiction in Teenagers

The Impact of Internet Addiction in Teenagers

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Published: Sep 5, 2023

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Causes and triggers of internet addiction, consequences of internet addiction, prevention and intervention strategies, professional support and mental health awareness.

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Internet Addiction

Reviewed by Psychology Today Staff

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.

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Behavioral Addictions pp 119–145 Cite as

Internet Addiction

• Halley M. Pontes 4 ,
• Jason Satel 5 &
• Almuth McDowall 4  
• First Online: 18 September 2022

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

This chapter reviews the current literature on internet addiction (IA) and provides a comprehensive summary regarding: (i) potential positive and negative effects of internet and technology use, (ii) main conceptual frameworks, (iii) biological bases, (iv) comorbidity factors, (v) prevalence rates, (vi) assessment methodologies, and (vii) treatment approaches. Although the current evidence suggests a relatively low prevalence rate of IA worldwide, and that several scholars remain doubtful about the validity and utility of IA as a clinical phenomenon, the existing evidence indicates that further research is required in order to facilitate greater understanding of this intricate issue and to tackle a range of challenges identified in the literature. Furthermore, the current scientific trend points toward the adoption of more specific terms that underscore the role of specific online activities in eliciting addictive usage, as opposed to the adoption of the broad and unspecific umbrella term IA.

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5.1 Introduction

5.1.1 background.

In January 2021, nearly 60% of the global population (i.e., 4.66 billion individuals) were active internet users (Statista 2021 ). Furthermore, about 4.28 billion were unique mobile internet users while 4.14 billion were active social media users (Statista 2021 ). These figures indicate that the internet is a critical tool in today’s society permeating all aspects of people’s lives.

Through the use of the internet, people can easily communicate with each other regardless of their physical locations, search for information online, check their email, engage in synchronous or asynchronous communications, use social networking sites (SNSs), carry out work and/or study, play video games, manage their finances through online banking, order goods online, find romantic partners, watch movies, sports, and/or shows, etc. The internet provides many tools that can enhance our lives with beneficial effects at several levels including but not confined to greater reach and connectivity. However, due to its pervasiveness, scholars have become interested in investigating the potential positive and negative effects stemming from internet use at psychological, social, and biological level (Griffiths et al. 2016a ; Kuss and Pontes 2019 ; Marin et al. 2021 ; Pontes et al. 2015 ).

5.1.2 Internet Use: Positive Effects

When it comes to the benefits people experience in the context of internet use, several studies have reported a many of the advantages brought about by internet use. The internet presents the potential to promote healthy lifestyles and to support coping and management strategies of comorbidities. In a recent nation-wide study conducted among older age groups in Germany, Quittschalle et al. ( 2020 ) found that internet use among older people was associated with higher quality of life and lower levels of depressive symptoms. Moreover, additional benefits for older users reported in the literature include improvements in social connectivity (Choi and DiNitto 2013 ), prevention of social isolation (Chen and Schulz 2016 ), and greater access to information about leisure activities (Näsi et al. 2012 ). For younger users, the current evidence suggests that the use of SNSs can lead to lower levels of depression (Wang et al. 2019 ) as well as positive educational, social, and psychological outcomes for students (Rayan et al. 2017 ). Finally, in terms of positive psychological well-being effects, internet adoption has been shown to boost life satisfaction (Lissitsa and Chachashvili-Bolotin 2016 ).

5.1.3 Internet Use: Negative Effects

Notwithstanding the many positive effects that internet use can produce, a large body of evidence has reported deleterious effects (further discussed in Sect.  5.3 ). One of the most commonly investigated negative effects stemming from internet use includes aggression, especially in the context of violent video games (Coyne and Stockdale 2020 ; Verheijen et al. 2021 ). The well-known issue at the heart of this chapter is related to addiction in the context of excessive or problematic internet use (PIU), also termed ‘internet addiction’ (IA) (Griffiths 1996 ; Young 1996 ), which refers to detrimental use of the internet with negative consequences to the users (see Sect.  5.1.4 : Conceptual frameworks for a review of the current definitions). Throughout this chapter, the term IA will be adopted for the sake of consistency.

Although research on IA is rapidly evolving, it is not a new field of research. The first reports on IA were published over two decades ago. For instance, Griffiths ( 1996 ) argued that researchers should focus on the particular addictive nature of the internet by identifying the primary behaviors associated with such addiction (e.g., gaming, emailing, information seeking, pornography, socializing, etc.). In his seminal work, Griffiths ( 1996 ) suggested that the internet should be conceptualized as a medium in which the addictive process takes place. In that same year, Young ( 1996 ) published a case report of a 43 year old woman who exhibited symptoms of IA and proposed defining and assessing IA by adapting the criteria for substance dependence from the Diagnostic and Statistical Manual of Mental Disorders (4th ed.) DSM-IV (American Psychiatric Association 1994 ).

Although this chapter treats IA as an addictive disorder, we advocate a critical perspective as IA is not currently recognized officially as a mental health disorder by official medical bodies such as the American Psychiatric Association or the World Health Organization. In fact, many researchers have argued that IA is a misnomer and an inadequate construct that should be abandoned altogether due to current lack of knowledge about its nature (Starcevic 2013 ). For example, Starcevic and Aboujaoude ( 2016 ) argued that the role of internet as a medium (to fuel other online addictions) does not exist, despite the fact that the internet can play an important role in eliciting addictive behaviors.

However, it is possible that the concept of IA is too heterogeneous because it includes a wide range of different online behaviors (Starcevic and Aboujaoude 2016 ). This observation has lead to recommendations to replace the term IA with terms pertaining to specific online behaviors (e.g., gambling, SNS use, gaming, etc.). There is currently no consensus on the terminology that should be used to describe IA, with research referring to this phenomenon as ‘problematic internet use’ (Shapira et al. 2000 ), ‘pathological internet use’ (Suler 1999 ), ‘compulsive internet use’ (Greenfield 1999 ), and ‘internet use disorder’ (Geisel et al. 2013 ) even though they may not necessarily be synonyms (Starcevic and Billieux 2017 ). The next subsection (see Sect.  5.1.4 : Conceptual frameworks ) will provide an overview of the latest advances regarding the definition, theory, and conceptualization of IA.

5.1.4 Conceptual Frameworks

Although several definitions exist for IA, these have been extensively debated over the last 25 years. Nevertheless, all existing definitions and theoretical frameworks exploring this issue refer to IA as a behavioral addiction that is associated with serious functional and health-related impairments (Pontes et al. 2015 ). Early theoretical approaches suggested that IA was akin to pathological gambling, further defining it as an impulse-control disorder that does not involve the ingestion of psychoactive intoxicants (Young 1998b ). Similarly, Kuss and Pontes ( 2019 ) defined IA as a behavioral pattern of internet use marked by dysfunctional craving underpinning unregulated and excessive usage that can lead to significant psychosocial and functional impairments not accounted for by any other disorder, these may include jeopardizing social, academic, and professional activities, and the development of comorbidities such as depression and axiety.

Alternate definitions have been suggested by other scholars. For example, IA has been defined in terms of poorly controlled cognitive preoccupation, urges, and behaviors related to excessive internet use leading to clinical impairment and distress (Weinstein et al. 2014 ). Block ( 2008 ) defined IA as a compulsive-impulsive disorder associated with excessive patterns of computer use, experience of withdrawal, tolerance, and other deleterious outcomes. Griffiths ( 1995 ) has also suggested that IA is a type of ‘technological addiction’, operationally defined as a nonchemical (behavioral) addiction involving excessive human–machine interactions. Within this framework, technological addictions (e.g., IA) include the experience of six key components that are common to all addictive behaviors which are salience, mood modification, tolerance, withdrawal, conflict, and relapse (Griffiths 2005 ).

In terms of theories and models that have been developed to explain the nature of IA, several important contributions have been made thus far. Below, two prominent conceptual frameworks will be explored, the early Cognitive-Behavioral Model (Davis 2001 ) and the more recent Interaction of Person-Affect-Cognition-Execution Model (Brand et al. 2019 , 2016 ). We summarize three prevalent frameworks in Table 5.1 .

5.1.4.1 The Cognitive-Behavioral Model

The Cognitive-Behavioral Model (Davis 2001 ) refers to IA as ‘pathological internet use’ and makes a key contribution by distinguishing between specific pathological internet use (SPIU) and generalized pathological internet use (GPIU). This important distinction emerged from the idea that the internet can serve different purposes. SPIU is defined as a type of pathological use of the internet whereby individuals become addicted to a specific online function/application. Conversely, GPIU is defined as a general, multidimensional behavioral pattern of pathological overuse. In this model, maladaptive cognitions play a major role in the development and maintenance of pathological use of the internet.

This theory adopts the concept of distal and proximal contributory causes to explain pathological internet use and illustrate its etiological process. Here, distal causes refer to existing psychiatric conditions (such as depression, social anxiety, etc.) and the behavioral reinforcement provided by the internet when individuals engage with new functionalities alongside the situational cues that contribute to conditioned responses. In contrast, proximal causes are related to maladaptive cognitions that are understood to be a sufficient condition for pathological use (in terms of both GPIU and SPIU) and the subsequent impairments that emerge due to pathological use of the internet (Davis 2001 ).

In this model, Davis ( 2001 ) proposed that GPIU is expressed at the behavioral level through the expenditure of excessive amounts of time using the internet with no direct purpose. This is why procrastination is thought to play an important role in the development and maintenance of GPIU. Interestingly, recent evidence supports a robust link between IA and procrastination within different contexts of internet use, even beyond generalized use (Aznar-Díaz et al. 2020 ; Geng et al. 2018 ; Müller et al. 2020 ; You et al. 2020 ).

Furthermore, this model argues that symptoms related to pathological internet use are primarily due to the experience of maladaptive cognitions (Davis 2001 ). This is because these symptoms are primarily experienced at the cognitive level, and encompass obsessive thoughts about internet use, reduced ability to control impulses, an inability to stop internet use, and the experience of a subjective and generalized feeling that the internet is the only place where the individuals feel good about themselves (Davis 2001 ). Additional symptoms can include thinking about the internet when not online, anticipating future time online, reduced interest in other previously enjoyed activities, and experiencing social isolation (Davis 2001 ).

In conclusion, the Cognitive-Behavioral Model provides a robust understanding of IA, including a framework for its assessment, despite being a theoretical model and not a comprehensive theory of IA in itself. This model is prominent due to the extensive support received from previous research (e.g., Caplan 2002 , 2010 ) and its applicability to other internet-related problems such as Internet Gaming Disorder (IGD) (Haagsma et al. 2013 ). Additionally, this theory has inspired subsequent treatment approaches for IA (see Sect. 5.6 : Treatment Approaches ), including the cognitive behavior therapy for IA (CBT-IA) (Young 2011 , 2013 ).

5.1.4.2 The Interaction of Person-Affect-Cognition-Execution Model (I-PACE)

Brand et al. ( 2016 ) developed the Interaction of Person-Affect-Cognition-Execution (I-PACE) model and recently revised it (see Brand et al. 2019 ). The I-PACE model highlights the process in which internet use disorders intertwine with person-affect-cognition-execution. This model expanded upon the Neuropsychology-Based Model of Internet Addiction (see Brand et al. 2014 ) by focusing on specific types of IA, while also conveying the idea that individuals have a first choice use, similarly to the idea of a first-choice drug in substance use disorders.

The I-PACE model aims to explain at the theoretical level, the main processes implicated in the development and maintenance of specific IA to certain contents and features people experience online (Brand et al. 2016 ). The model was devised according to the following components: predisposing factors, affective and cognitive responses to internal or external stimuli, executive and inhibitory control, decision-making behavior resulting in the use of specific online content, and consequences regarding the use of the internet and websites of choice.

When explaining the nature of IA, the I-PACE model can be thought of as a process model arguing that specific forms of IA are a consequence of the interactions between predisposing factors (e.g., neurobiological and psychological constitutions), moderators (e.g., individual coping styles and cognitive biases), and mediators (e.g., affective and cognitive responses to situational triggers in combination with reduced executive functioning). Moreover, this model suggests that conditioning processes can reinforce these associations as they occur within an addiction process (Brand et al. 2016 ).

The I-PACE model includes key components illustrating this process. The first component (the P component) is related to the person’s biopsychological makeup, psychopathological characteristics, personality, social cognitions, and internet use motives. The second component (the A and C components) pertain to the affective and cognitive responses individuals display in response to external or internal stimuli. These allude to an individual’s coping ability, internet-related cognitive biases, cue reactivity and craving, urge for mood regulation, and attentional biases. The third component (the E component) relates to the person’s executive functions, inhibitory control, and decisions to use certain specific online features or websites. These denote impaired executive functions, inhibitory control, and decision making (Brand et al. 2016 ).

According to Brand et al. ( 2016 ), the decision to engage with specific online content or websites may lead to short-term positive experiences and gratifications, particularly in the early stages of the addiction process. The I-PACE model also suggests that the use of specific online content or websites, alongside the gratification experienced leads to greater cue reactivity and craving responses to certain stimuli as a result of both Pavlovian and instrumental-conditioning processes (Brand et al. 2016 ).

The I-PACE model proposes that some predisposing factors of IA are not plastic (e.g., genetic factors, early childhood experiences), whereas others may be difficult to change (e.g., psychopathological vulnerability factors, personality). One of the main implications of this model is that treatment approaches for IA should focus on addressing the moderating and mediating factors that can be targeted and modified across pharmacological and psychological treatments. As a result of the main propositions of this model, when treating individuals presenting with IA, consideration of key predisposing factors and systematic assessment of cognitive functions (e.g., attentional biases, implicit and explicit cognitions, executive functions, and inhibitory control capacities) should be prioritized.

Our current understanding of the nature of IA can thus be derived from different conceptual standpoints. Although two prominent models have been considered in this chapter, these are not the only theoretical frameworks developed so far. Therefore, interested readers are encouraged to expand their knowledge of the theoretical processes involved in IA through the examination of other important conceptual frameworks such as the Syndrome Model of Addiction (Shaffer et al. 2004 ), the Components Model of Addiction (Griffiths 2005 ), and/or the Neuropsychology-Based Model (Brand et al. 2014 ).

5.2 Biological Bases

Over the course of the past decade, researchers have begun unravelling the neurobiology of behavioral addictions, particularly in regard to IA (see Cerniglia et al. 2017 ; Park et al. 2017 ; Sharifat et al. 2018 ; Tereshchenko and Kasparov 2019 ). This section will briefly discuss some the key findings regarding the neurobiology of IA.

In the investigation of the biological underpinnings of IA, scholars have utilized a wide range of neuroimaging techniques in order to identify regions and processes in the human brain implicated in IA. Some of the imaging techniques researchers have adopted with high spatial resolution include structural magnetic resonance imaging (MRI) (e.g., voxel-based morphometry [VBM], diffusion tensor imaging [DTI]), functional magnetic resonance imaging (fMRI), and nuclear imaging (e.g., positron emission tomography [PET] and single photon emission computed tomography [SPECT]). Further studies have also been conducted using electroencephalography (EEG) to enhance our knowledge of the neurobiology of the temporal dynamics of neural activity underlying IA. For the purpose of conciseness, this section will not review EEG findings (see Burleigh et al. 2020 for a recent review).

When we consider the existing neurobiological evidence concerning IA, several important conclusions can be made. In terms of MRI findings, the current literature suggests that IA is commonly linked to decreased gray matter in a number of cortical regions (Tereshchenko and Kasparov 2019 ). Through the use of VBM, researchers have found that adolescents with IA present with lower gray matter density in the anterior cingulate cortex (ACC), left insula, left lingual gyrus, and left posterior cingulate cortex (PCC) (Zhou et al. 2011 ). Additional evidence has shown decreased gray matter density in the right orbitofrontal cortex (OFC), right supplementary motor area, and bilateral insula in patients diagnosed with IA (Weng et al. 2013 ). Another study of adolescents presenting with IA similarly observed reductions in gray matter density in the bilateral dorsolateral prefrontal cortex (DLPFC), OFC, supplementary motor area, cerebellum, and left rostral ACC (Yuan et al. 2011 ). Although these studies reported relatively similar results regarding decreased gray matter density among individuals with IA, the specific brain regions showing reduced gray matter were not entirely consistent across these studies, potentially reflecting methodological discrepancies related to assessment and recruitment of participants.

It is worth noting that several brain regions that have been reported to be altered in individuals with IA have been previously associated with functions related to the development of other addictive behaviors. For instance, damage to the prefrontal cortex (PFC) is commonly observed in addictive behaviors, and among IA individuals, gray matter atrophy of the PFC has been associated with loss of control related to internet use, which is a key characteristic of IA (Park et al. 2017 ). Interestingly, the OFC regulates impulse control and decision-making, while the dorsolateral PFC and rostral ACC are typically responsible for cognitive control (Krawczyk 2002 ).

When examining the neurobiology of IA, researchers have also employed DTI paradigms to quantify the status of white matter tracts via fractional anisotropy (FA), which measures the diffusivity of water molecules within the brain (Park et al. 2017 ). Accordingly, previous research has found that FA was increased in the thalamus and left PCC of individuals presenting with specific types of IA (i.e., IGD) in comparison to healthy controls (Dong et al. 2012a ). Further research has demonstrated that FA of IA individuals was lower in a number of brain regions (e.g., the orbitofrontal white matter, corpus callosum), and that no brain region presented with higher FA levels when compared to controls (Lin et al. 2012 ). Yuan et al. ( 2011 ) also found that FA of white matter in the right parahippocampal gyrus was decreased, and that FA was increased in the left posterior limb of the internal capsule among IA individuals.

In a more recent study, Cheng and Liu ( 2020 ) used resting-state fMRI and DTI techniques to investigate the functional and structural connectivity of the amygdala in individuals with IA. This study found that individuals with IA presented with decreased negative functional connectivity (FC) between the amygdala and the DLPFC, and increased negative FC between the amygdala and precuneus and superior occipital gyrus (SOG). Furthermore, Cheng and Liu ( 2020 ) reported that IA individuals in their study showed decreased positive FC between the amygdala and ACC and had increased positive FC between the amygdala and thalamus. Interestingly, the authors also reported that the duration of IA was linked with the FC between the left amygdala and right DLPFC. Overall, Cheng and Liu ( 2020 ) concluded that amygdala connectivity was altered in IA individuals, and that altered FC between the amygdala and DLPFC was correlated with the duration of IA. Taken together, this emerging body of evidence suggests that IA can lead to significant neurobiological impairments in both white and gray matter, and that observed changes to these structures may be associated with greater risk of developing IA. Nevertheless, most of the existing evidence is not causal in nature and therefore caution when interpreting such results is warranted.

Studies using fMRI techniques have led to the suggestion that increased activity in brain areas such as PFC and ACC is associated with impulsivity and craving responses in IA individuals (Park et al. 2017 ). For example, using an event-related fMRI Stroop color-word paradigm to assess inhibitory control, Dong et al. ( 2012b ) found that male IA individuals exhibited greater blood-oxygen level-dependent (BOLD) signaling in the ACC and dorsal PCC in the incongruent Stroop trials, when compared to a control group (Dong et al. 2012b ).

Current evidence also suggests that increased dorsal PCC activity is associated with incomplete disengagement of the default mode network, and impairment in the optimization of tasks related to attentional resources among IA individuals (Park et al. 2017 ). In another fMRI study, Dong et al. ( 2011 ) examined the differences in reward and punishment processing among male individuals presenting with IA as compared to healthy controls. In this study, participants presenting with IA showed significant increases in OFC activity in gain trials, and decreases in ACC activity in loss trials (Dong et al. 2011 ). This finding suggests that there is an increase in reward sensitivity and a decrease in loss sensitivity associated with IA since the OFC has been previously shown to be activated by reward (Gallagher et al. 1999 ), while the ACC is activated by losses (Petrovic et al. 2008 ).

Researchers have also conducted nuclear imaging research to further understand IA. Particular attention has been paid to research employing both SPECT and PET nuclear imaging techniques. Overall, the current evidence stemming from PET research supports the occurrence of impairments within the dopaminergic system in the brains of individuals experiencing IA (Park et al. 2017 ). In one key PET study (Kim et al. 2011 ), researchers assessed the D2 dopamine receptor availability of individuals presenting with IA using the radioligand 11C-raclopride. Results showed that participants with IA presented with reduced dopamine D2 receptor availability in the bilateral caudate and left putamen in comparison to participants in the control group, suggesting that reductions in dopamine activity are associated with IA (Kim et al. 2011 ). Interestingly, hypodopaminergic function (i.e., reduced dopamine D2 receptor) has been implicated in obesity in human and animals, and reward deficiency leading to increased reward seeking behaviors (e.g., compulsive eating) to compensate for the diminished dopamine activity (Beeler et al. 2016 ).

The dopamine transporter is a plasma membrane protein that is involved in the transport of dopamine from extracellular space into presynaptic neurons (Torres et al. 2003 ). Interestingly, in one study (Hou et al. 2012 ), researchers assessed the levels of dopamine transporter in individuals experiencing IA using 99mTc-TRODAT-1SPECT and reported that levels of striatal dopamine transporters decreased among those experiencing IA as compared to non-IA individuals. In a similar vein, comparable findings have been widely reported among individuals experiencing substance use disorders (Verma 2015 ; Zahniser and Sorkin 2004 ). In summary, the current evidence suggests that IA may lead to impairments in the dopaminergic systems in the brain indicating evidence for long term consequences for the nervous system, further impacting behaviors, mental health, and well-being.

Analysis of the current evidence suggests that IA is associated with both structural of functional impairments in the OFC, dorsolateral PFC, ACC, and PCC regions of the brain. These areas are implicated in many higher-level functions, such as the processing of reward, motivation, memory, and cognitive control abilities (Park et al. 2017 ). Furthermore, IA has also been associated with impairments of dopamine D2 receptor function, which is related to dysregulation of the OFC. Since these findings share important commonalities with those from the substance use disorder field, it is likely that IA and substance use disorders share specific underlying neurobiological mechanisms, although these may vary according to the behavior and substance being investigated (Park et al. 2017 ).

5.3 Comorbidities

For many years, researchers have been particularly interested in expanding our understanding about how IA may co-occur with other mental health disorders. The current literature supports consistent associations between IA and a wide range of psychiatric disorders, including but not limited to mood disorders (e.g., major depressive disorder, bipolar disorder), social anxiety, sleep but also neurodiverse conditions including attention-deficit/hyperactivity disorder (ADHD) disorders and autism spectrum disorder (ASD) (Carli et al. 2012 ; Karaca et al. 2017 ; Pluhar et al. 2019 ).

5.3.1 Why Does IA Co-Occur with Other Psychopathologies?

When we consider the dynamic relationship between IA and other comorbidities, previous theoretical models such as the I-PACE model (Brand et al. 2019 , 2016 ) and the Cognitive-Behavioral Model (Davis 2001 ) suggested that existing psychopathologies and dysfunctional personality traits are key factors leading to the development of IA. Furthermore, Kuss and Pontes ( 2019 ) posited that the relationship between IA and other psychiatric disorders such as depression is bi-directional. In the case of mood disorders such as depression, this bi-directional association occurs as depressed individuals are more likely to become addicted to the internet because excessive internet use helps them manage daily stressors (e.g., low mood) (Morita et al. 2021 ).

This bi-directional process is justified by the fact that one of the key characteristics of behavioral addictions such as IA relates to its mood enhancing properties through the well-known mood modification capabilities of internet use (Griffiths 2005 ). Moreover, the deleterious effects, antecedents, and negative outcomes typically associated with IA (e.g., social isolation, loneliness, interpersonal conflict) (see Mamun et al. 2020 ; Tian et al. 2020 ; Zhang et al. 2017 ; Zhou et al. 2017 ) can further trigger and exacerbate ongoing depressive symptoms suggested by the social displacement hypothesis (Bessière et al. 2008 ; Kraut et al. 1998 ) which holds that time spent on the internet is to the detriment of high quality relationships.

5.3.2 Mood Disorders in IA

The existent literature suggests that individuals diagnosed with IA experience greater levels of depression when compared to non-IA individuals, with almost one in three individuals experiencing major depressive disorder also presenting with IA (Alpaslan et al. 2016 ). Furthermore, a study by Wölfling et al. ( 2015 ) reported that the overall prevalence of bipolar disorders among IA individuals was about 5.6%, with about 30.9% of those experiencing IA also having bipolar spectrum disorder diagnosis.

5.3.3 Neurodiversity in IA

A range of conditions such as autism, dyslexia, attention deficit hyperactivity disorder and Tourette’s syndrome are now increasingly referred to as neurodiversity which was a movement originated by Judy Singer to frame human experience as a biological spectrum rather than collection of disorders. Our understanding of relevant conditions continues to advance, and in particular to two conditions linked to IA. ADHD was originally thought to be affecting young boys in particular, and refer to a range of difficulties linked to attention and impulsivity and has high prevalence rates. For example, ADHD affects over 10% of all children and adolescents in Australia and over a third of all these individuals will retain symptoms into adulthood (Visser et al. 2014 ). Additionally, ADHD has been reported to affect between 7.47% of children and adolescents in Africa (Ayano et al. 2020 ) and 6.26% of youth in China (Wang et al. 2017b ). This condition is now recognized as a lifespan condition (Matheson et al. 2013 ) where it is common for ADHD-ers Footnote 1 to demonstrate hyperfocus on particular interests. Perhaps not surprisingly, previous research has also shown important links between IA and ADHD.

In a review of the relationship between IA and ADHD, Karaca et al. ( 2017 ) reported that the prevalence of ADHD among those experiencing IA ranged between 26.8 and 83.3%. Conversely, Karaca et al. ( 2017 ) also found that the co-occurrence of IA among ADHD-ers ranged from 15.7% to 71.8%.

Wang et al. ( 2017a ) conducted a meta-analytic study reviewing the evidence from a total of 15 empirical studies investigating the relationship between IA and ADHD and found that IA was moderately associated with ADHD, and that those with IA were more likely to experience severe levels of ADHD (with high rates of inattention and impulsivity). In a more recent longitudinal study investigating the causal pathway between ADHD and IA, Zhou et al. ( 2020 ) found that ADHD levels measured at time 1 predicted IA levels at time 2 but not vice versa. Furthermore, the authors reported that those diagnosed with ADHD were more likely to experience IA than controls, suggesting that ADHD is a key risk factor for IA (Zhou et al. 2020 ). In summary, the present evidence provides a robust indication that IA and ADHD are highly comorbid.

5.3.4 ASD in IA

Similarly to ADHD, ASD is also associated with focus on particular interests, as well as restrictions in social communication and repetitive behaviors. Research on ASD and IA was prompted by early studies on internet use reporting that socially isolated individuals present with high levels of internet use (e.g., Sanders et al. 2000 ). In one of the first studies investigating this relationship, Romano et al. ( 2014 ) reported a correlation between the presence of autistic traits, IA and anxiety, where the relationship between IA and autistic traits is moderated by anxiety—where those with high anxiety use the internet less. Given the study design, further research is needed to disentangle any causal relationships however.

Research exploring the associations between ASD and IA has been conducted in children, adolescents, and adult populations both in terms of generalized and specific IA. More recently, So et al. ( 2019 ) recruited a clinical sample and conducted a two-year longitudinal study investigating the relationship between IA, ASD, and ADHD among Japanese adolescents that had been clinically diagnosed. According to the results, the prevalence of IA at baseline was about 8.9% (n = 5) among participants classed as ASD only, 6.7% (n = 1) among participants classed as ADHD only, and about 22.22% (n = 4) among participants classed as both ASD and ADHD (So et al. 2019 ).

The relationship between ASD and IA has also been supported by clinical data. Engelhardt et al. ( 2017 ) conducted a study on a sample of 119 individuals with and without ASD to investigate the relationship between IGD and ASD. The authors found that participants with ASD spent significantly more time playing video games than non-ASD participants, and that the former group showed greater levels of IGD when compared to the latter group. These findings led Engelhardt et al. ( 2017 ) to conclude that the risk of specific IA related to online gaming is greater in adults with ASD than in non-ASD adults. Additional research is needed  to investigate long-term effects of prolonged and sustained internet use and whether any effects are different to other sustained interests also present in ASD adults.

Further evidence exists supporting the link between IA and ASD, particularly among younger internet users. In one study, Kawabe et al. ( 2019 ) recruited 55 Japanese children and adolescents that had been diagnosed with ASD. Based on the clinical and psychometric assessments performed, the authors found that IA was prevalent in nearly half of all ASD participants (45%, n = 25). Kawabe et al. ( 2019 ) also concluded that hyperactivity symptoms in ASD adolescents might be an important factor explaining the onset of IA. Therefore, the evidence regarding co-occurrence for ASD generalized and specific IA in adolescents and adults, especially among those presenting with IGD warrants future investigation.

5.3.5 Social Anxiety in IA

The link between IA and social anxiety, which comprises fears about meeting other people and their judgment, has also been researched as developmental stages are known to play an important role in both social anxiety and internet use (Prizant-Passal et al. 2016 ). Notwithstanding this, the current evidence suggests that the relationship between IA and social anxiety is complex. In their review study, Prizant-Passal et al. ( 2016 ) found that although social anxiety was not associated with email use or instant messaging, it was linked with online gaming. Moreover, Prizant-Passal et al. ( 2016 ) found a significant association between IA and social anxiety, suggesting that social anxiety may be particularly prominent among high-severity IA individuals.

In a longitudinal study investigating a total of 2,293 adolescents, Ko et al. ( 2009 ) found that depression, ADHD, social anxiety, and hostility predicted IA in a two-year follow-up period, with hostility and ADHD being the most significant predictors of IA. Furthermore, in a large-scale study involving a sample of 1,460 students aged between 11 and 15 years, Yayan et al. ( 2016 ) found that about 13.7% of their sample presented with IA, and that there was a positive association between IA and social anxiety. Moreover, the authors noted that although IA was mostly associated with online gaming, dating, and browsing the internet, social anxiety was specifically related to homework, online gaming, and browsing the internet (Yayan et al. 2016 ). Finally, Peterka-Bonetta et al. ( 2019 ) found that social anxiety and impulsivity also presented positive associations with higher levels of IA and substance use disorder, further cementing the current body of evidence supporting the relationship between IA and social anxiety across different age groups.

5.3.6 Sleep Disorders in IA

Another important phenomenon co-occurring with IA is related to sleep disorders. The relationship between IA and sleep disorders is multidimensional in nature, with the current evidence suggesting that IA may be implicated in different types of sleep disorders since both frequent internet use (Li et al. 2010 ) and excessive exposure to blue light electronic devices can negatively affect sleep due to its interferences with melatonin secretion (Chellappa et al. 2013 ). Previous research has demonstrated important links between IA and sleep disturbances such as short sleep duration (Guo et al. 2018 ), reduced sleep quality (AlAmer et al. 2020 ), insomnia (Tsumura et al. 2018 ), increased fatigue (Bener 2017 ), and daytime drowsiness (Alimoradi et al. 2019 ).

In a recent meta-analysis, Alimoradi et al. ( 2019 ) reviewed a total of 23 empirical studies examining the association between IA and sleep disturbances (i.e., sleep problems and duration). The authors found that individuals with IA were approximately 2.20 times more likely to experience sleep problems when compared to non-IA individuals. Additionally, Alimoradi et al. ( 2019 ) found that IA individuals presented with reduced overall sleep duration when compared to those without IA (overall pooled standard mean difference = −0.24 h). In a similar vein, with a sample of 1976 adolescents Wang et al. ( 2021 ) found that those with IA presented with significantly higher risk of developing behavioral and emotional problems than those without IA, with sleep disorders partially mediating this effect.

Further epidemiological evidence from review studies have shown a positive association between IA and specific sleep disorders, including insomnia, short sleep duration, and suboptimal sleeping quality (e.g., Lam 2014 ). Additionally, a recent study conducted among 4750 school-based adolescents found that IA was associated with greater risk of sleep disturbance with older adolescents having higher risk of experiencing sleep disturbances (Yang et al. 2018 ).

In terms of the association between IA and insomnia, several studies have been conducted using different samples. In a study by Cheung and Wong ( 2011 ), the authors found that about 51.7% of adolescents with IA were also identified as insomniacs and about 58.9% were depressed. The authors also found that both insomnia and IA were significantly associated with depressive symptoms, implying that a complex underlying mechanism exists between insomnia, IA, and depression (Cheung and Wong 2011 ). Similarly, another study reported that adolescents presenting with IA were significantly more likely to exhibit higher levels of insomnia, stress, anxiety, depression, and low self-esteem (Younes et al. 2016 ).

In addition to the studies discussed above, recent research has shown that IA affects other sleep-related factors that are important to maintaining adequate sleep health. For example, in a sample of emerging adults, Jahan et al. ( 2019 ) found that IA was associated with poor sleep quality. Furthermore, Jahan et al. ( 2019 ) found that those experiencing moderate and severe levels of IA were 75% and 95% less likely to have good sleep quality, respectively. Similarly, Karimy et al. ( 2020 ) found that young adults with higher levels of IA presented with poorer sleep quality compared to those without IA. Taken together, the current evidence suggests important links between IA and sleep disorders through excessive and dysregulated internet use.

5.4 Prevalence Rates

Although IA is not yet an officially recognized mental health disorder, several epidemiological studies have been conducted to estimate the different levels of prevalence rates of IA across many cultural contexts. This line of research is paramount to helping estimate the current demand for consulting, treatments, and preventative measures since a large number of empirical reports have found that IA affects a small minority of internet users (Griffiths et al. 2016b ; Pontes et al. 2015 ).

Several review studies have been conducted to clarify the extent of problems caused by IA in different parts of the globe. In one of the most recent review and meta-analytic studies conducted to date examining the epidemiological evidence on IA from 113 studies (published from 1996 to 2018) that included a total of 693,306 individuals from 31 countries, the authors found a prevalence rate of 7.02% for IA and 2.47% for IGD (Pan et al. 2020 ). Furthermore, among healthcare professionals, a review study conducted by Buneviciene and Bunevicius ( 2020 ) found a pooled prevalence rate of IA of 9.7% based on a sample of 1,818 healthcare professionals, with IA being strongly associated with greater mental health symptoms and fatigue among healthcare workers.

In an earlier systematic review study conducted by Kuss et al. ( 2014 ) to assess the evidence from a total of 68 epidemiological studies on IA (published after the year of 2000) that included at least 1,000 individuals, the authors found prevalence rates of IA ranging from 0.8% in Italy to 26.7% in Hong Kong. Furthermore, the authors also noted that no gold standard of IA classification was found, with 21 different assessment tools used to assess the construct in these studies (Kuss et al. 2014 ). Moreover, Kuss et al. ( 2014 ) reported that these tools were based on the substance use disorder and/or pathological gambling clinical criteria, and that the majority of them had no (or few) relevant criteria for behavioral addiction diagnosis, time spent on the internet, or functional impairment.

In a meta-analytic study conducted by Cheng and Li ( 2014 ) assessing the overall prevalence of IA based on a total of 80 eligible epidemiological studies conducted across 31 nations from seven world regions, a worldwide prevalence rate of IA of 6%, with the highest rates observed in the Middle East (10.9%) and lowest rates in Northern and Western Europe (2.6%). Interestingly, Cheng and Li ( 2014 ) also found that decreased quality of life was linked to higher prevalence rates of IA.

In another similar study, Pontes et al. ( 2015 ) reviewed the prevalence rates of IA reported from 12 robust empirical studies (published between 2014 to 2015) using nationally representative data from different countries. According to this review, prevalence rates of IA ranged from a minimum of 0% in Iran to a maximum of 18.7% in Taiwan (Pontes et al. 2015 ). Although all these studies were culturally diverse, all studies presented with several limitations as they adopted a cross-sectional design, had substantial heterogeneity in the way assessment of IA was conducted, and showed arbitrariness in terms of the cut-off points used to estimate prevalence rates of IA, even when studies adopted the same assessment tool for IA (Pontes et al. 2015 ).

Further epidemiological research has been conducted to assess the prevalence rates of IA in different countries using nationally representative data. Among adolescents, an overall prevalence rate of IA of 26.5% has been reported by Xin et al. ( 2018 ), with severe addiction levels affecting about 0.96% of individuals (based on a large nationally representative sample of Chinese adolescents [N = 6,648, age range = 10–18 years]). In Korea, Kim et al. ( 2020 ) found an overall prevalence rate of IA of 5.2% (7.7% among males and 3.8% among females) based on 22,542 adolescents (age range = 12–18 years). In Slovenia, (Macur et al. 2016 ) IA has been reported to affect 3.1% of the adult population and 20% of adolescents in the 8 th grade (Pontes and Macur 2021 ). Similarly, Lewczuk et al. ( 2020 ) found, in a nationally representative sample of Polish adults (N = 1,036, age range = 18–69 years), that the prevalence of self-perceived IA (i.e., not assessed through a standardized diagnostic tool) was 23% and 4.2% for social media addiction.

In summary, based on the current epidemiological evidence on IA, it can be concluded that robust studies using large and nationally representative samples assessing IA on different cohorts are still scarce. However, this type of research is important to broaden our current understanding of how IA may impact different populations. Furthermore, discrepancies in the prevalence rates observed in previous research seem to be primarily due to research limitations (e.g., assessment strategy, study design, sampling technique) (Pontes et al. 2015 ).

5.5 Assessment Approaches

In terms of potential assessment approaches, several psychometric and clinical tools have been developed to assist the assessment of IA (Pontes et al. 2015 ). Traditionally, a wide range of assessment tools have been employed to assess IA, and this section will cover some of the main assessment tools that interested researchers and clinicians can adopt in their daily work.

One of the first assessment tools used in empirical research to assess and diagnose IA is the eight-item Diagnostic Questionnaire (DQ) (also known as Young’s Diagnostic Questionnaire [YDQ]) (Young 1998b ), which uses a dichotomous scoring system (i.e., ‘yes’ or ‘no’). This tool was adapted from the criteria for pathological gambling as defined in the DSM-IV (American Psychiatric Association 1994 ). An example of an item includes: “ Do you feel restless, moody, depressed, or irritable when attempting to cut down or stop internet use? ” (see Young 1998b for a list of all items). In terms of diagnostic capabilities, this tool specifies that responding ‘yes’ to at least five out of the eight questions indicates IA.

The DQ represents an important early effort towards facilitating assessment of IA and inspired follow-up research. However, there is very little empirical and clinical evidence supporting the suggested cutoff points initially proposed. For example, Wartberg et al. ( 2017 ) found that the DQ presented with low levels of reliability, and concluded that assessing IA with the DQ may not be accurate as measurements across time may differ. Despite this, parental assessment of IA (especially in children and adolescent samples) is also possible using the DQ since Wartberg et al. ( 2016 ) developed a parental version of this tool with a nationally representative sample from Germany (N = 1,000 parents of adolescents aged 12–17 years). The parental version of the DQ adopts the same diagnostic principles of the original DQ, but all eight items must be responded to by parents or caretakers. All relevant information (e.g., items, scoring) about the parental DQ can be found within its original study (see Wartberg et al. 2016 ).

The most popular assessment tool for IA is the Internet Addiction Test (IAT) (Young 1998a ). The IAT contains a total of 20 items measuring characteristics and behaviors associated with compulsive use of the internet and its impairments. Once completed, a score ranging from 0 to 100 can be obtained, with higher scores indicating greater severity of IA, and the following cutoff points being proposed: ‘none’ (0–30), ‘mild’ (31–49), ‘moderate’ (50–79), and ‘severe’ (80–100) (Young 1998a ). The IAT has received substantial cross-cultural scrutiny, and has been developed in several languages, such as Pakistani (Waqas et al. 2017 ), Portuguese (Pontes et al. 2014 ), Turkish (Kaya et al. 2016 ), Polish (Hawi et al. 2015 ), and other languages.

The psychometric properties of the IAT have been well documented in the literature, through several studies. More specifically, a systematic review and meta-analysis conducted by Moon et al. ( 2018 ) reviewing the evidence from 25 studies using the IAT, concluded that the test presented with excellent levels of internal consistency (Pooled Cronbach’s alpha ranging from 0.83 to 0.93), with the authors suggesting that the IAT has acceptable test–retest reliability, and convergent validity in specific samples. However, its clinical validity has been questioned by research reporting that the IAT presents with low diagnostic accuracy in clinical samples since it is only able to detect 42% of individuals who have already been clinically diagnosed with IA (Kim et al. 2013 ).

More recent assessment approaches have focused on the development of assessment tools for IA based on the DSM-5 criteria for IGD (American Psychiatric Association 2013 ). One such example is the Internet Disorder Scale–Short Form (IDS9-SF) (Pontes and Griffiths 2016 ), which includes nine items that can be used to assess broad symptoms of IA. According to the authors, endorsement of at least five out of the nine items may indicate IA. The IDS9-SF has been developed in other languages, such as Italian (Soraci et al. 2020 ) and Bangla (Saiful Islam et al. 2020 ). Overall, these studies report adequate construct validity and satisfactory levels of internal consistency.

Although there are several psychometric tests available to assess IA, previous review studies have criticized some of the existing assessment tools. A review by Király et al. ( 2014 ) of the nine most used IA psychometric tools reported different types of limitations that underscore inconsistencies across the instruments in terms of (i) underlying theoretical bases, (ii) factor structures, and (iii) psychometric properties. Moreover, Lortie and Guitton ( 2013 ) reviewed a total of 14 IA assessment tools and reported that they varied considerably on several important aspects. Specifically, the following three dimensions were present across all tools: compulsive use of the internet, negative outcomes, and salience. However, less commonly assessed dimensions among these tools related to using the internet to relieve adverse moods and withdrawal symptoms (Lortie and Guitton 2013 ).

In summary, although the field has developed several assessment tools for IA that have been widely adopted across numerous countries, more progress needs to be achieved to establish a sound assessment framework for IA since current tools appear to lack clinical validity and there is no established gold standard for diagnosing IA (Kuss et al. 2014 ). This means that diagnosis and identification of IA is largely left to clinical judgment and therefore the respective practitioners’ awareness and training.

5.6 Treatment Approaches

Despite the relatively low prevalence of IA, affected individuals often seek professional help for their internet-related problems, with some countries providing psychiatric facilities entirely dedicated to treating IA (Zajac et al. 2017 ). Therefore, evaluating the existing treatment approaches is important to provide evidence supporting specific treatments.

To date, treatment for IA has been mainly conducted through psychological and pharmacological approaches. However, regardless of the chosen treatment approach, complete abstinence from the internet should not be the goal of any specific intervention, instead, regulated use of the internet should be achieved (Pontes et al. 2015 ). In terms of treatments, a meta-analysis conducted by Winkler et al. ( 2013 ) concluded that in relation to the efficacy of treatments for IA, effect sizes were high, robust, and maintained over a follow-up period.

In terms of pharmacotherapy, treatment methods have adopted the use of antidepressants such as escitalopram (Dell'Osso et al. 2008 ) and bupropion (for IGD) (Bae et al. 2018 ), opioid receptor antagonists such as naltrexone (for internet sex addiction) (Bostwick and Bucci 2008 ), and antipsychotics such as quetiapine (combined with citalopram) (Atmaca 2007 ). Furthermore, psychotherapy-based approaches for IA have demonstrated promising results employing Cognitive Behavioral Therapy (CBT) over 15 (Wölfling et al. 2014 ) and 12 (Young 2013 ) sessions, family therapy over six (Liu et al. 2015 ) and 14 sessions (Zhong et al. 2011 ), and using positive psychology interventions over 10 sessions (Khazaei et al. 2017 ).

In a systematic review study conducted by Przepiorka et al. ( 2014 ) on the existing evidence for CBT and pharmacological treatments of IA, the authors recommended that therapists should adopt both treatment approaches since a combined strategy has been found to be the most effective method for treating IA.

To summarize, despite the relatively high amount of treatment studies on IA published to date, previous review studies have suggested a paucity of well-designed treatment research studies of IA (Kuss and Lopez-Fernandez 2016 ; Winkler et al. 2013 ). Moreover, pharmacological treatments for IA are still in development as very little is known about their efficacy. Despite the promising results so far, psychotherapy treatments studies have also been hindered by weak methodological standards, leading some authors to conclude that there are currently no treatments for IA that fully meet the criteria for being considered evidence-based or even a possibly efficacious intervention (Zajac et al. 2017 ).

5.7 Final Conclusions

The present chapter reviewed the current literature on IA and attempted to provide a comprehensive understanding of several aspects pertaining to this issue, namely its: potential positive and negative effects, main conceptual frameworks, biological bases, comorbidity factors, prevalence rates, assessment methodologies, and treatment approaches employed thus far.

Despite the large amount of research that has been conducted on IA, current approaches and theoretical understanding of this phenomenon suggest that the term IA in itself is problematic when used to understand the issue as a broad mental health condition. Instead, researchers seem to favor the adoption of more precise and specific terminology to describe different forms of IA linked to unique online activities, implying that IA is better conceptualized as a spectrum than a single entity (Starcevic and Billieux 2017 ).

Due to the existing conceptual and theoretical challenges hindering the recognition of IA as a bona fide addictive disorder, it is likely that official medical bodies (e.g., World Health Organization and American Psychiatric Association) will focus more on the future merits of specific forms of IA based on the addictive potential and subsequent functional impairments of specific online activities when revising their diagnostic manuals. Notwithstanding the current conceptual challenges and lack of medical recognition, we believe that prematurely abandoning IA may be may lead to serious unintended harmful psychosocial consequences. This is because the existing scientific conundrum should not be used to generate further stigma by invalidating peoples’ own experiences of distress in relation to dysregulated digital technology use. There are groups of adolescents and adults who engage in internet use to an extent which is harmful to their wellbeing. The onus is on researchers to collaborate with practitioners, including clinicians, psychologists and therapists to devise accessible and evidence-based treatment approaches. This is pramount given also the increased vulnerability of individuals with mental health conditions and neurodiverse people.

We deploy ‘identity first language’ consistent with current preferences of those affected.

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How to Know If You Have an Internet Addiction and What to Do About It

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

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• Top 5 Things to Know

Internet Addiction in Kids

• What to Do If You're Addicted

Internet addiction is a behavioral addiction in which a person becomes dependent on the Internet or other online devices as a maladaptive way of coping with life's stresses.

Internet addiction has and is becoming widely recognized and acknowledged. So much so that in 2020, the World Health Organization formally recognized addiction to digital technology as a worldwide problem, where excessive online activity and Internet use lead to struggles with time management, sleep, energy, and attention.

Top 5 Things to Know About Internet Addiction

• Internet addiction is not yet an officially recognized mental disorder. Researchers have formulated diagnostic criteria for Internet addiction, but it is not included in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5-TR) . However, Internet Gaming Disorder (IGD) is included as a condition for further study, and Internet addiction is developing as a specialist area.
• At least three subtypes of Internet addiction have been identified: video game addiction , cybersex or online sex addiction, and online gambling addiction .
• Increasingly, addiction to mobile devices, such as cellphones and smartphones, and addiction to social networking sites, such as Facebook, are being investigated. There may be overlaps between each of these subtypes. For example, online gambling involves online games, and online games may have elements of pornography.
• Sexting , or sending sexually explicit texts, has landed many people in trouble. Some have been teens who have found themselves in hot water with child pornography charges if they are underage. It can also be a potential gateway to physical infidelity .
• Treatment for Internet addiction is available, but only a few specialized Internet addiction services exist. However, a psychologist with knowledge of addiction treatment will probably be able to help.

If you or a loved one are struggling with an addiction, contact the Substance Abuse and Mental Health Services Administration (SAMHSA) National Helpline at 1-800-662-4357 for information on support and treatment facilities in your area.

For more mental health resources, see our National Helpline Database .

As Internet addiction is not formally recognized as an addictive disorder, it may be difficult to get a diagnosis. However, several leading experts in the field of behavioral addiction have contributed to the current knowledge of symptoms of Internet addiction. All types of Internet addiction contain the following four components:  

Excessive Use of the Internet

Despite the agreement that excessive Internet use is a key symptom, no one seems able to define exactly how much computer time counts as excessive. While guidelines suggest no more than two hours of screen time per day for youths under 18, there are no official recommendations for adults.

Furthermore, two hours can be unrealistic for people who use computers for work or study. Some authors add the caveat “for non-essential use,” but for someone with Internet addiction, all computer use can feel essential.

Here are some questions from Internet addiction assessment instruments that will help you to evaluate how much is too much.

How Often Do You...

• Stay online longer than you intended?
• Hear other people in your life complain about how much time you spend online?
• Say or think, “Just a few more minutes” when online?
• Try and fail to cut down on how much time you spend online?
• Hide how long you’ve been online?

If any of these situations are coming up on a daily basis, you may be addicted to the Internet.

Although originally understood to be the basis of physical dependence on alcohol or drugs, withdrawal symptoms are now being recognized in behavioral addictions, including Internet addiction.

Common Internet withdrawal symptoms include anger, tension, and depression when Internet access is not available.   These symptoms may be perceived as boredom, joylessness, moodiness, nervousness, and irritability when you can’t go on the computer.

Tolerance is another hallmark of alcohol and drug addiction and seems to be applicable to Internet addiction as well.   This can be understood as wanting—and from the user's point of view, needing—more and more computer-related stimulation. You might want ever-increasing amounts of time on the computer, so it gradually takes over everything you do. The quest for more is likely a predominant theme in your thought processes and planning.

Negative Repercussions

If Internet addiction caused no harm, there would be no problem. But when excessive computer use becomes addictive, something starts to suffer.

One negative effect of internet addiction is that you may not have any offline personal relationships, or the ones you do have may be neglected or suffer arguments over your Internet use.

• Online affairs can develop quickly and easily, sometimes without the person even believing online infidelity is cheating on their partner.
• You may see your grades and other achievements suffer from so much of your attention being devoted to Internet use.
• You may also have little energy for anything other than computer use—people with Internet addiction are often exhausted from staying up too late on the computer and becoming sleep deprived.
• Finances can also suffer , particularly if your addiction is for online gambling, online shopping, or cybersex.

Internet addiction is particularly concerning for kids and teens. Children lack the knowledge and awareness to properly manage their own computer use and have no idea about the potential harms that the Internet can open them up to. The majority of kids have access to a computer, and it has become commonplace for kids and teens to carry cellphones.

While this may reassure parents that they can have two-way contact with their child in an emergency, there are very real risks that this constant access to the Internet can expose them to.

• Children have become increasingly accustomed to lengthy periods of time connected to the Internet, disconnecting them from the surrounding world.
• Children who own a computer and have privileged online access have an increased risk of involvement in cyberbullying , both as a victim and as a perpetrator.  
• Children who engage in problematic internet use are more likely to use their cellphone for cybersex, particularly through sexting, or access apps which could potentially increase the risk of sex addiction and online sexual harms, such as Tinder.  

In addition, kids who play games online often face peer pressure to play for extended periods of time in order to support the group they are playing with or to keep their skills sharp. This lack of boundaries can make kids vulnerable to developing video game addiction.   This can also be disruptive to the development of healthy social relationships and can lead to isolation and victimization.

Children and teens are advised to have no more than two hours of screen time per day.

What to Do If You Have an Internet Addiction

If you recognize the symptoms of Internet addiction in yourself or someone in your care, talk to your doctor about getting help. As well as being able to provide referrals to Internet addiction clinics, psychologists, and other therapists, your doctor can prescribe medications or therapy to treat an underlying problem if you have one, such as depression or social anxiety disorder.

Internet addiction can also overlap with other behavioral addictions, such as work addiction, television addiction , and smartphone addiction.

Internet addiction can have devastating effects on individuals, families, and particularly growing children and teens. Getting help may be challenging but can make a huge difference in your quality of life.

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Reid Chassiakos YL, Radesky J, Christakis D, Moreno MA, Cross C. Children and Adolescents and Digital Media . Pediatrics . 2016;138(5):e20162593. doi:10.1542/peds.2016-2593

Musetti A, Cattivelli R, Giacobbi M, et al. Challenges in Internet Addiction Disorder: Is a Diagnosis Feasible or Not ?  Front Psychol . 2016;7:842. doi:10.3389/fpsyg.2016.00842

Walrave M, Heirman W. Cyberbullying: Predicting Victimisation and Perpetration . Child Soc . 2011;25:59-72. doi:10.1111/j.1099-0860.2009.00260.x

Gámez-Guadix M, De Santisteban P. "Sex Pics?": Longitudinal Predictors of Sexting Among Adolescents . J Adolesc Health. 2018;63(5):608-614. doi:10.1016/j.jadohealth.2018.05.032

Hilgard J, Engelhardt CR, Bartholow BD. Individual differences in motives, preferences, and pathology in video games: the gaming attitudes, motives, and experiences scales (GAMES) . Front Psychol. 2013;4:608. doi:10.3389/fpsyg.2013.00608

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By Elizabeth Hartney, BSc, MSc, MA, PhD Elizabeth Hartney, BSc, MSc, MA, PhD is a psychologist, professor, and Director of the Centre for Health Leadership and Research at Royal Roads University, Canada.  

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Internet Addiction: Causes, Effects, And Treatments

What Is An Internet Addiction?

• Signs Of Internet Addiction
• Causes Of Internet Addiction
• Risk Factors
• Effects And Consequences
• Co-Occurring Disorders
• Is Internet Addiction Real?
• Treatment Options
• Find Treatment For Internet Addiction

Internet addiction disorder (IAD) is a type of behavioral addiction that involves compulsive Internet use. People with an Internet addiction may have underlying mental health or substance use issues that may require specialized addiction treatment.

According to the Pew Research Center, 93 percent of adults in the United States use the Internet. Among teenagers, Internet use is likely even higher.

Internet use has become what some might call a necessity in daily life. For some people, however, Internet use—including social media and online gaming—can become a compulsive and even addictive habit.

While not officially recognized as a disorder in the United States, compulsive Internet use is believed to be fairly common, affecting an estimated 1.5 to 8.2 percent of people in North America.

Read more about the causes and treatment of behavioral addictions

Internet addiction, also known as Internet addiction disorder (IAD), is a behavioral addiction characterized by compulsive, uncontrollable Internet use that interferes with daily life.

Types of Internet addiction include:

• online gambling addiction
• cybersex addiction
• video game addiction
• social media addiction

Regular use of the Internet is common and even necessary for many occupations and academic pursuits. In addition, the Internet is also used to form or maintain social connections.

When a person feels unable to control their Internet use, however, and continues to do so despite negative effects on their life, this may be a sign of a problem.

Signs Of Internet Addiction Disorder

Using the Internet very often, or enjoying being online, are not signs of an addiction by themselves.

An addiction is generally characterized by repetitive behaviors that interfere with a person’s daily life, and that the person feels unable to control.

If you’re concerned about your Internet use, or that of someone else, there are several common signs and behaviors researchers have identified among people with Internet addiction.

Signs and symptoms of Internet addiction might include:

• excessive Internet use (i.e. spending a majority of time online)
• staying online for longer than intended
• lying about the extent of one’s Internet use
• unsuccessful attempts to limit Internet use
• neglecting relationships with others due to Internet use
• experiencing disruptions in work or academic pursuits as a result of Internet use
• experiencing guilt, shame, or frustration about one’s Internet use
• continuing to spend the majority of time online despite negative effects on physical or mental health

Psychological withdrawal symptoms have also been reported by people with compulsive Internet use. For example, feeling very on-edge, hostile, or anxious when unable to access a computer.

Causes Of Internet Addiction Disorder

Increased use of mobile technologies and the Internet for everyday activities is not by itself a cause for Internet addiction.

Although this is an ongoing subject of research, researchers currently believe Internet addiction could be influenced by genetic, biological, and interpersonal factors.

For instance:

• abnormalities in neurochemical processes
• history of mental illness or a personality disorder
• personal or family history of addiction
• Internet access and availability

One theory underlying Internet addiction, the quality of real life theory, suggests that people who experience difficulties in their offline lives may turn to the Internet to escape or avoid reality.

Therefore, people who have stressful lives, or are unhappy with their lives, may be more likely to turn to the Internet to cope.

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Risk Factors For Internet Addiction

Certain types of people are believed to be potentially more vulnerable to developing an Internet addiction compared to the general population, based on proposed risk factors.

Risk factors for Internet addiction include:

• history of mental health disorder
• history of substance misuse
• young age (e.g. child, teenager, young adults)
• genetic predisposition

Effects And Consequences Of Internet Addiction

Living with an addiction to the Internet can be isolating. While many people use the Internet and social media to connect online, some research shows this can actually increase loneliness.

People who become addicted to the Internet may experience distress over time, as their use becomes more compulsive and they become more disconnected from their offline reality.

In addition, other consequences of an Internet addiction might include:

• disrupted sleep patterns
• neglect of personal hygiene
• poor eating habits
• relationship troubles
• decreased work or academic performance
• vision problems
• psychological withdrawal symptoms

Behavioral addictions such as compulsive Internet use can be progressive. This means the effects and consequences of internet addiction may grow more severe with time.

Internet Addiction And Co-Occurring Disorders

It’s not uncommon for a person who compulsively uses the Internet to also have another type of mental health disorder.

Co-occurring disorders might include:

• alcohol use disorder
• drug abuse and addiction
• anxiety disorders
• major depression
• technological addictions (e.g. addiction to smartphones or television)

Internet Addiction And Substance Abuse

Most people who hear the term ‘addiction’ typically think of drug or alcohol addiction. According to some research, substance abuse and behavioral addictions can be connected.

Alcohol use disorder, in particular, is believed to be associated with compulsive Internet use, particularly among college students.

Drugs, alcohol, and the Internet can for some share a similar function: to numb, escape, or manage feelings or realities they’d prefer to avoid.

Is An Internet Addiction Real?

The existence of ‘Internet addiction’ is somewhat controversial. An increasing number of health professionals recognize that Internet addiction is a legitimate problem.

In South Korea, for instance, Internet addiction has been recognized by authorities as a national health problem. In the Middle East, Internet addiction is also believed to be fairly widespread.

Researchers in the United States have advocated for the inclusion of Internet addiction disorder within the Diagnostic and Statistical Manual of Mental Disorders (DSM), which is used to diagnose mental health and related conditions.

While it’s not currently recognized as an independent disorder, Internet addiction is a growing specialist area among mental health treatment providers who recognize its detrimental impact.

Treatment For Internet Addiction

Seeking treatment for Internet addiction may be necessary for people who feel unable to reduce their Internet use on their own.

The types of treatments recommended for Internet addiction can vary according to a person’s medical history, mental health history, and other personal factors.

Treatment options for Internet addiction might include:

• behavioral therapy
• mental health counseling
• group therapy
• family therapy
• digital detoxification (detox)
• self-help groups

Treatment may focus on helping a person overcome their compulsive Internet use by addressing its connection to emotions, thought patterns, and other behavioral tendencies.

For people with co-occurring substance use issues, a dual diagnosis treatment program through a substance abuse treatment center may also be recommended.

Dual Diagnosis Treatment For Internet Addiction And Substance Abuse

Dual diagnosis treatment is a type of treatment that aims to address all co-occurring mental health issues a person experiences, such as substance misuse and compulsive Internet use.

Dual diagnosis treatment can be effective for addictions, as well as mental health conditions like depression, anxiety, or a history of trauma.

Finding Treatment For Internet Addiction

If you or a loved one is struggling with compulsive Internet use, one of our addiction resource specialists may be able to help.

By calling our helpline, we can:

• identify appropriate treatment options
• verify your insurance
• find a treatment program that meets your needs

Call us today to find treatment for internet addiction , including dual diagnosis rehab and mental health treatment options.

Written by the Addiction Resource Editorial Staff

Addiction Resource aims to provide only the most current, accurate information in regards to addiction and addiction treatment, which means we only reference the most credible sources available.

These include peer-reviewed journals, government entities and academic institutions, and leaders in addiction healthcare and advocacy. Learn more about how we safeguard our content by viewing our editorial policy.

• American Psychiatric Association (APA)—New Research Press Briefing: Internet Addiction: Review of Neuroimaging Studies https://www.psychiatry.org/newsroom/news-releases/internet-addiction-review-of-neuroimaging-studies
• NPR News—Hooked On The Internet, South Korean Teens Go Into Digital Detox https://www.npr.org/2019/08/13/748299817/hooked-on-the-internet-south-korean-teens-go-into-digital-detox
• Pew Research Center—Demographics of Internet and Home Broadband Usage in the United States https://www.pewresearch.org/internet/fact-sheet/internet-broadband/
• U.S. National Library of Medicine—Internet Addiction: A Brief Summary of Research and Practice https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3480687/
• U.S. National Library of Medicine—Internet Addiction Prevalence and Quality of (Real) Life: A Meta-Analysis of 31 Nations Across Seven World Regions https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4267764/
• U.S. National Library of Medicine: PubMed—The association between harmful alcohol use and Internet addiction among college students: comparison of personality https://pubmed.ncbi.nlm.nih.gov/19335391/
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Essay on Internet Addiction

Students are often asked to write an essay on Internet Addiction in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Internet Addiction

Introduction.

Internet addiction is a growing problem globally. It refers to excessive use of the internet, leading to negative impacts on a person’s life.

The main cause of internet addiction is the desire for online social interaction and entertainment. Online games, social media, and websites can be very engaging.

Internet addiction can lead to poor academic performance, lack of social skills, and health issues like eye strain and obesity due to physical inactivity.

It’s important to balance internet usage with other activities. Parents and teachers can help by setting limits and promoting healthy habits.

Also check:

• Paragraph on Internet Addiction

250 Words Essay on Internet Addiction

The advent of the internet has revolutionized human existence, providing limitless opportunities for learning, communication, and entertainment. However, this unprecedented access to information and connectivity has birthed a new form of dependency – internet addiction.

Understanding Internet Addiction

Internet addiction, also known as compulsive internet use, is characterized by excessive or poorly controlled preoccupations, urges, or behaviors regarding computer use and internet access. It is a psychological disorder that can lead to severe stress, anxiety, and a variety of other mental health problems.

Causes and Effects

The causes of internet addiction are multifaceted, ranging from the need for social interaction, escapism, or the thrill of exploring virtual realities. The effects, however, can be detrimental, leading to academic failure, job loss, and the breakdown of personal relationships.

Prevention and Treatment

Prevention is always better than cure. Encouraging healthy internet usage habits, promoting physical activities, and fostering real-life social interactions can help prevent this addiction. However, once addicted, professional help may be necessary. Cognitive-behavioral therapy has proven effective in treating internet addiction by helping individuals to identify and change patterns of thought that lead to compulsive behaviors.

In conclusion, while the internet has undoubtedly brought about vast benefits, it has also introduced new challenges. Internet addiction is a growing concern that requires our attention. By understanding its causes and effects, we can develop strategies to prevent and treat this modern-day affliction.

500 Words Essay on Internet Addiction

Internet addiction, also known as compulsive internet use, has emerged as a significant issue in the digital age. It is a psychological condition that involves excessive use of the internet, resulting in negative impacts on an individual’s life.

Internet addiction is characterized by an individual’s inability to control their use of the internet, which eventually interferes with their daily life, work, and relationships. It is not merely about the amount of time spent online but the obsession with internet activities to the point where it affects mental and physical health, personal relationships, and productivity.

Causes and Symptoms

The causes of internet addiction can be multifaceted. It can be a symptom of other underlying mental health issues like depression, anxiety, and stress disorders. The anonymity, ease of access, and perceived environment of acceptance and escape the internet offers can also contribute to its addictive potential. Symptoms may include preoccupation with the internet, inability to control online use, neglect of personal life, and emotional changes such as restlessness or irritability when internet use is limited.

Impacts of Internet Addiction

Internet addiction can have severe impacts. It can lead to a sedentary lifestyle, which can result in obesity, cardiovascular issues, and other health problems. It can also lead to sleep disorders due to late-night internet use. From a psychological perspective, it can increase feelings of loneliness, depression, and anxiety. It can also lead to academic or job failures due to a lack of concentration and reduced productivity.

Preventing internet addiction involves promoting healthy internet use. This can be achieved by setting time limits, taking regular breaks, and promoting a balanced lifestyle with physical activities and offline social interactions. Treatment for those already addicted often involves cognitive-behavioral therapy, which helps individuals identify problematic behaviors and develop coping strategies. In severe cases, medication may also be used under professional supervision.

In conclusion, internet addiction is a growing concern that requires attention. As we continue to embrace digital technology, it is crucial to promote healthy internet use and provide help for those struggling with addiction. It’s a call to action for researchers, mental health professionals, and society as a whole to understand and address this modern-day issue effectively.

That’s it! I hope the essay helped you.

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• Essay on Evolution of Internet
• Essay on Life Without Internet
• Essay on Influence of Internet on Children

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• Open access
• Published: 09 April 2024

Correlation of negative emotion, fatigue level and internet addiction in college students: implication for coping strategies

• Shanshan Gu 1 ,
• Xue Min 1 ,
• Jing Xu 1 &
• Shu Chen 2  

BMC Psychiatry volume  24 , Article number:  264 ( 2024 ) Cite this article

173 Accesses

Metrics details

Internet addiction has an important influence on the development of physical and mental health of college students. The purpose of this study is to evaluate the current status and the correlation between college students’ negative emotion, fatigue level and Internet addiction disorder, and to provide reference for the care and management of college students.

We conducted a questionnaire survey on a cluster sample of college students from October to November 15, 2022. Internet addiction scale, fatigue assessment scale and positive and negative emotion scale were used for survey. Pearson correlation analysis and mediating effect test were performed to analyze the correlation and effects.

A total of 1546 valid questionnaires were collected. The incidence of internet addiction in college student was 20.38%. The total score of internet addiction was 52.94 ± 12.47, the total fatigue score was 69.27 ± 3.19, the score of positive emotion of college students was 31. 41 ± 5.09, and the negative emotion score was 18.54 ± 5.68. The total score of internet addiction were positively correlated with score of negative emotion (all P  < 0. 05). The total score of internet addiction scale of college students were positively correlated and each factor score of with the score of fatigue severity (all P  < 0. 05). Fatigue played an intermediary role in the prediction of negative emotion and internet addiction of college students, with an intermediary role of-0.433, accounting for 76.35% of the total effect.

The college students’ positive emotion may be strengthened to reduce their fatigue level and negative emotion so as to reduce internet addiction.

Peer Review reports

With the rapid popularization and development of the internet, people begin to pay attention to the social reality problem of internet addiction, especially adolescent internet addiction [ 1 ]. College students or teenagers are susceptible to internet addiction. The survey results [ 2 ] show that 20.6% of college students are at risk of internet addiction, the surveyed college students use mobile phones for an average of 7 to 9 h a day, with an average of 118 mobile phones per person per day. Previous researches [ 3 , 4 ] show that the proportion of college students with internet addiction is as high as 16. 5%. 6% ∼ 29.5%. Previous studies [ 5 , 6 ] have found that internet addiction can damage people’s executive control ability, emotional recognition ability and central integration ability. Internet addicts are more likely to process negative social cognition [ 7 ]. Teenagers with negative withdrawal tendencies are also more likely to have internet addiction [ 8 ]. Therefore, the prevention and care of internet addiction are vital to the health development of teenagers.

Under the sustained consumption of cognitive and emotional will, people may experience a decline in psychological function and subjective experience of fatigue and tiredness [ 9 , 10 ]. Therefore, emotion, especially negative emotion, is easy to make teenagers escape into the world of the internet [ 11 ]. Previous studies [ 12 , 13 ] have reported that long-term excessive use of the internet has an impact on the mental health of adolescents, the emotions and fatigue may be closely associated with internet addiction. Currently, there are very few studies on the correlation of negative emotion, fatigue level and internet addiction in college students. Therefore, we aimed to conduct a cross-sectional study to evaluate the current status and potential correlation of negative emotion, fatigue level and internet addiction in college students. This study assumed that fatigue played an intermediary role between college students’ emotions, especially negative emotions and internet addiction.

This cross-sectional survey was approved by the Ethics Committee of the University (Ethics approval number: 2022zsy-kj-11). Written informed consents had been obtained from all the included students. According to previous report [ 14 ], the routine sample size was determined by 10  ∼  20 times of the analytical factor and this study required more than 56 items × 20 = 1120 samples. In order to reduce the error and expand by 30% on this basis, this study needed at least about 1456 students.

From October to November 15, 2022, we conducted a questionnaire survey on a cluster sample of college students in a university in Hangzhou, Zhejiang province, China. We randomly selected the corresponding number of students according to the proportion of college students in each grade. The inclusion criteria of the students were as follows: full-time college students; they were currently studying normally in our college; they volunteered to participate in this study.

All the questionnaires in this study were investigated anonymously. The questionnaires used are as follows: (1) We designed a general information questionnaire for medical students, including medical students’ age, gender, body mass index (BMI), whether the student was the only child of family, parents’ educational level. (2) Internet addiction scale [ 15 ]: In this scale, 26 questions were assessed with 4 grades, including tolerance symptom, withdrawal symptom, forced to surf the Internet, interpersonal health, time management. The higher the score, the more serious respondents are addicted to the Internet. The scale was widely used in the study of Internet addiction with good reliability and validity [ 16 ]. If the total score of the internet addiction scale was ≥ 63, the respondents are assessed to have internet addiction. The construct validity of the scale was good (comparative fit index (CFI) = 0. 96), and the internal consistency coefficient of the scale was 0.91 [ 17 ]. (3) Fatigue assessment scale (FAI) [ 18 ]: There are 29 declarative sentences, which are scored from 1 to 7 and scored at seven points, including four subscales: the severity of fatigue, the environmental specificity of fatigue, the results of fatigue and the response of fatigue to rest and sleep. the higher the score, the higher the degree of fatigue. It has been reported that FAI is easy to operate and can accurately evaluate the degree and characteristics of fatigue. The construct validity of the scale was good (CFI = 0. 95), and the internal consistency coefficient of FAI scale was 0.93 [ 19 ]. (4) Positive and negative emotion scale (PANAS) [ 20 ]: This scale was compiled by Watson and Clark of South Meaddist University in 1988 to evaluate individuals’ positive and negative emotions. The Chinese version of the positive and negative emotion scale was translated and verified by Huang Li et al. The scale contains 20 adjectives reflecting emotion, and 10 words correspond to positive and negative emotion factors respectively. The two factors were statistically scored, in which the higher the score of positive emotion, the more positive emotion and more concentration. On the contrary, the higher the score of negative emotion is, the more painful it is, and the more negative emotion is. The construct validity of the PANAS scale was good (CFI = 0. 88), and the internal consistency coefficient of the scale was 0.97 [ 21 ].

Survey procedures

Before collecting the questionnaire, we adjusted the instruction of the questionnaire and the answer format of the questionnaire to minimize the resistance and fatigue of the students when filling out the questionnaire, and to ensure that the subjects answered the questions carefully on the basis of understanding the meaning of the questions. We introduced the purpose of this study to students, and emphasized that this questionnaire answered anonymously, abided by the principle of confidentiality, the data collected was only for scientific research, there was no difference between right and wrong, and they can choose according to their own real situation. The filling time of the questionnaire was limited to 20 min, and the surveyors checked the questionnaire data on the spot. If there were missing data, the students were required to fill in the questionnaire. If the students were unwilling, the questionnaire would be invalidated.

Statistical analysis

In this study, SPSS 22.0 was used to analyze the data, the counting data were expressed by case and frequency, and the measurement data were expressed by mean ± standard deviation. Independent sample t-test was used to compare the measurement data between the two groups. To understand the potential correlation and interaction of negative emotion, fatigue level and internet addiction in college students, Pearson correlation analysis was used to analyze the relationship between groups of measurement data to ensure the feasibility of subsequent testing of hypothetical model fitting. And mediating effect test and Bootstrap method were used to analyze the intermediary role of fatigue in negative emotion and internet addiction. In this study, P  < 0.05 indicating that the difference between groups was statistically significant.

Initially 1580 questionnaires were distributed and a total of 1546 valid questionnaires finally were collected. The characteristics of included college students are presented in Table  1 .

As shown in Table  2 , the total score of internet addiction was 52.94 ± 12.47, 315 students’ internet addiction score ≥ 63, the incidence of internet addiction in college student was 20.38%.

As shown in Table  3 , the total fatigue score was 69.27 ± 3.19, it showed that the fatigue level of college students was in the middle level.

The score of positive emotion of college students was 31. 41 ± 5.09, and the negative emotion score was 18.54 ± 5.68. As shown in Table  4 , the total score and each factor score of internet addiction score of college students were negatively correlated with the score of positive emotion (all P  < 0. 05), the total score of internet addiction scale and the scores of all factors were positively correlated with the score of negative emotion (all P  < 0. 05). The total score and each factor score of internet addiction scale of college students were positively correlated with the score of fatigue severity (all P  < 0. 05).

As shown in Fig.  1 ; Table  5 , fatigue played an intermediary role in the prediction of l negative emotion and internet addiction of college students, with an intermediary role of-0.433, accounting for 76.35% of the total effect.

The chart on the mediating effect of fatigue on negative emotion and internet addiction of college students

Discussions

With its unique advantages and speed of development, the network is changing our way of working, learning and thinking, permeating every corner of our daily life, and bringing us into a new era. However, the network is a “double-edged sword”. Its negative effect is the same as its positive effect, which involves all aspects of social life [ 22 , 23 ]. For contemporary college students, the Internet has become an important means of learning knowledge, finding information, chatting, making friends, satisfying personal hobbies and understanding current events [ 24 ]. How to give full play to the positive role of the Internet, avoid its negative effects as far as possible, minimize the Internet addiction behavior of college students, and put forward intervention measures according to its influencing factors and possible consequences, it is the focus of this study to provide a basis for medical departments, education departments, parents, college students and relevant departments of society to build network civilization [ 25 ]. The results of this study show that college students with more positive emotions are not prone to internet addiction, while college students with more negative emotions are prone to internet addiction. The more serious the fatigue is, the more likely it is to become internet addictive. And fatigue as an intermediary factor will further deepen the impact of negative emotions on internet addiction.

Fatigue is the response of the body to long-term continuous activity or mental load, it can affect the nervous and endocrine system, increases the secretion of epinephrine and cortisol hormones, which have a direct negative effect on mood [ 26 ]. In addition, fatigue also reduces the body’s absorption of folic acid and vitamin B12, which play an important role in the body’s metabolism and cell growth [ 27 , 28 ]. In addition, fatigue can cause different degrees of physiological reactions, such as accelerated heartbeat, elevated blood pressure, shortness of breath, etc., making people feel irritable, anxious, or depressed, which in turn exacerbates the feeling of fatigue [ 29 ]. Another condition that is easy to detect is that fatigue can affect the quality of sleep, which in turn aggravate the sense of fatigue, fall into a bad cycle that ca. not break free, and even lead to mental illness in the long run [ 30 , 31 ]. Therefore, the early intervention of fatigue is of great significance.

It has found that when the individual is in a state of mental fatigue, the negative emotion usually increases and it is difficult to concentrate [ 32 ]. When facing negative emotions, college students need to carry out emotional regulation, and emotional regulation will damage college students’ limited resources of self-control, which is a kind of ability to control and restrain their own emotions. People’s self-control, like muscles, has an upper limit, when beyond this limit, people’s behavior is easy to get out of control [ 33 , 34 , 35 ]. According to the theory of limited self-control, the loss of self-control will lead to the decline of college students’ ability to resist hedonistic experiences, so they are prone to internet addiction [ 36 , 37 ].

The basic explanation of the loss of compensation hypothesis for internet addiction is that internet rational compensation leads to loss of compensation and Internet addiction behavior [ 38 ]. Both negative emotion and fatigue will increase the loss of self-control of college students, and college students will follow the hypothesis of loss of compensation to try to get entertainment and relaxation from the Internet, but in fact, the loss of self-control makes college students unable to better control their online behavior, which is easy to lead to internet addiction, and excessive use of the internet makes the brain highly tense for a long time, which is easy to make people tired [ 39 , 40 , 41 ]. Fatigue further stimulates internet addiction, which forms a vicious circle. This study supports the views of previous studies [ 42 , 43 , 44 ] on the relationship between emotion and Internet addiction. Therefore, in order to reduce the formation of college students’ internet addiction and reduce the impact of negative emotions on college students’ internet addiction, solving college students’ fatigue may be the key question, and college students need to learn to care for themselves and be kind to themselves. And previous studies [ 45 , 46 , 47 ] have also found that the incidence of suicidal ideation in patients with internet addiction will increase, so the phenomenon of internet addiction among college students needs to attract further attention from college students themselves, their families, schools and society.

Prevention first, early intervention should be a coping strategy to reduce college students’ internet addiction. As an important work, we should focus on finding high-risk groups to provide more assistance. It includes goal setting, self-suggestion and reminder, aversion therapy, diversion, making a personal goal list, and so on. Students with addictive tendencies are grouped into long and small groups to adjust and improve their relations with others, learn new attitudes and behaviors, reduce fatigue levels, and correctly understand and treat life in the form of group counseling [ 48 , 49 ]. Besides, it is necessary to establish a linkage between schools and professional institutions, correctly identify patients with serious internet addiction, and refer them to professional medical institutions for active treatment [ 50 , 51 ]. At present, the comprehensive treatment of drug therapy combined with psychological counseling has been widely used in clinic, and the practice has proved to be an effective treatment measure [ 52 ].

This study reveals the mechanism of fatigue and negative emotion on college students’ internet addiction, but there are some limitations need further consideration. First of all, the sample of this study comes from a single university, the sample size is small, and the sample needs to be expanded to evaluate the generalizability of the findings. Secondly, this study is a questionnaire survey, which cannot prove the causal relationship, the findings should be treated with cautions. Follow-up researches using the experimental design method or longitudinal studies are needed to understand the mechanism of college students’ internet addiction disorder and elucidate causality in the future.

Conclusions

In summary, this study has found that the phenomenon of internet addiction among college students is more serious. Negative emotion not only directly affects the degree of internet addiction of college students, but also can affect the degree of internet addiction of college students through the intermediary effect of fatigue. Colleges and universities may strengthen network education and management, adopt various forms to widely carry out network knowledge publicity, network psychology lectures, network psychology square consultation and other activities, so that students and counselors can identify internet addiction and understand intervention measures. Psychological counselors with addictive tendencies should carry out systematic and long-term psychological intervention, adopt the combination of collective psychological counseling and individual psychological counseling, and pass through professional counselors to help visitors know themselves, accept themselves, appreciate themselves, overcome growth obstacles and develop their personal potential. School administrators and teachers should strengthen the guidance and construction of college students’ positive emotion and reduce their fatigue level and negative emotion so as to reduce the occurrence of internet addiction.

Data availability

All data generated or analyzed during this study are included in this published article.

Abbreviations

body mass index

fatigue assessment scale

positive and negative emotion scale

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Acknowledgements

This study was funded by 2022 Provincial Federation of Social Science research project (No.: 2022N118).

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Shanshan Gu, Xue Min & Jing Xu

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S G, X M designed research; S G, X M, J X, S C conducted research; S G, X M analyzed data; S G, X M, S C wrote the first draft of manuscript; S C had primary responsibility for final content. All authors read and approved the final manuscript.

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Gu, S., Min, X., Xu, J. et al. Correlation of negative emotion, fatigue level and internet addiction in college students: implication for coping strategies. BMC Psychiatry 24 , 264 (2024). https://doi.org/10.1186/s12888-024-05711-5

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Received : 18 October 2023

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Published : 09 April 2024

DOI : https://doi.org/10.1186/s12888-024-05711-5

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