research paper on internet addiction

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Published: 08 December 2023

Effects of different interventions on internet addiction: a systematic review and network meta-analysis

Yuqiong Zhu 1 na1 ,
Haihan Chen 2 na1 ,
Junda Li 1 ,
Xian Mei 3 &
Wenjuan Wang 1

BMC Psychiatry volume 23 , Article number: 921 ( 2023 ) Cite this article

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Globally, Internet is a recognized form of leisure, but there are growing apprehensions about the increasing number of individuals developing an addiction to it. Recent research has focused on social issues associated with internet addiction (IA). However, the treatment of IA is currently unclear. This study aimed to explore the relationship between IA treatment outcomes and different intervention strategies through systematic review and data analysis of patients who received different intervention modes.

A meta-analysis was conducted using RevMan 5.4 and Stata 14.2 on 57 literature research data from five Chinese and English databases, PubMed, Embase, Web of Science, Wanfang and CNKI.

A total of 57 randomized controlled trials (RCTs) were included in this network meta-analysis involving 3538 IA patients and 13 different interventions. The network meta-analysis results demonstrated that the top four interventions were: rTMS + CBT, drug + others, rTMS, and electro-acupuncture + CBT.

Our study indicated that comprehensive therapy had an optimal therapeutic effect on IA patients and rTMS + CBT ranked first among all therapeutic indicators of intervention, indicating optimal clinical effectiveness.

Peer Review reports

The internet has revolutionized communication, work, and access to information, becoming an integral part of modern life with numerous benefits and conveniences for users. However, this ubiquitous technology also has a darker side. Concerns about excessive Internet use have been raised in recent years, leading to the concept of Internet addiction (IA). According to statistics, the incidence of IA among Chinese college students is 11% [ 1 ]. IA was first proposed by psychologist Goldberg I. In 1996, Young confirmed that IA should be a true clinical psychological disorder [ 2 ]. IA was defined as the uncontrolled behavior of accessing the internet without substance, manifested as significant social and psychological impairment of individuals due to excessive use of the internet [ 3 ]. Although IA has not yet been formally incorporated into the framework of psychopathology, it is a potential problem both in terms of prevalence and public awareness, with many similarities to existing recognized barriers. Some studies have shown that IA may be related to abnormal activity in multiple brain regions and neurotransmitter systems, such as the prefrontal cortex, amygdala, ventral prefrontal cortex, striatum, and hippocampus [ 4 ]. The Anterior Cingulate Cortex is associated with cognitive control, impulse control, and attention regulation, and may play a role in regulating and controlling behavior in IA [ 5 ]. The amygdala is involved in emotional regulation and reward processing, and may play a role in emotional factors and reinforcement mechanisms in IA [ 6 ]. Symptoms of IA may include loss of control over internet use, preoccupation with online activities, neglect of personal responsibilities, and withdrawal symptoms when internet use is reduced or eliminated [ 7 ]. A range of negative consequences are associated with IA, including impaired social functioning, academic and work-related problems, and physical and mental health problems. Research has shown that IA is associated with symptoms of ADHD and depressive disorders [ 8 ].

In 2013, Internet gaming addiction (IGD) was first introduced by the American Psychiatric Association in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and nine diagnostic criteria for IGD were listed [ 9 ]. Although both IA and IGD are related to internet usage, IGD is a specific form of IA, which is addiction to internet games. Therefore, IGD is a subcategory of IA.

While cognitive-behavioral therapy (CBT), medication, and group therapy are common treatments for IA, it is true that there is a lack of data on their effectiveness. This is because research on IA treatment is still relatively new and ongoing. More research is needed to fully understand the most effective treatments for IA and how to tailor treatment approaches to individual needs. Research on IA treatment can help us determine which interventions are most effective for individuals with IA and improve prognosis. In the meantime, clinicians may use a combination of different treatments and strategies to help individuals with IA manage their symptoms and improve their quality of life. This article summarizes the effects of different intervention models on IA and explores the relationship between intervention models and effects.

This study focuses on randomized controlled trials (RCTs) as the research object. This study employed network meta-analysis methods to compare the effectiveness of different treatment methods for treating IA. This study aims to provide reference and evidence-based medicine data for clinical diagnosis and therapy by evaluating the impacts of various intervention models on IA and examining the relationship between various intervention techniques and treatment outcomes.

Materials and methods

Inclusion and exclusion standard.

Inclusion criteria: (1) Published studies on randomized controlled trials on IA, regardless of whether allocation concealment and blinding were mentioned in Chinese or English language; (2) Study subjects meeting one of two criteria: (a) Diagnosis criteria for IGD in DSM-5, Young's Diagnostic Questionnaire for IA, 1997 American Psychological Association Diagnostic Criteria for IA, or other clinical diagnostic criteria for IA; (b) A score of 40 or higher on either the Internet Addiction Test (IAT) or Chen's Internet Addiction Scale (CIAS) [ 10 , 11 , 12 ]; (3) Various scores from relevant internet addiction scales were used as evaluation indicators for the treatment effects of different intervention measures and their combinations; (4) Data extraction was limited to studies with full texts only.

Exclusion criteria: (1) Review systematic reviews; (2) Duplicate literature and non-peer-reviewed material; (3) Studies with outcome indicators that failed to meet the inclusion requirements or had apparent errors or omissions.

Search strategy

To identify studies that comply with the inclusion criteria, computer searches were performed in several databases, including PubMed, Embase, Web of Science, Wanfang, and China’s National Knowledge Infrastructure (CNKI). The databases were searched up to December 31, 2022, using a combination of controlled vocabulary and free text terms according to each database's search rules. The search terms included: Internet gaming addiction, electronic gaming addiction, online gaming, pathological internet use, addictive internet use, gaming disorder, internet addiction, excessive internet use, computer game addiction, internet dependence, efficacy, randomized, drug therapy, psychotherapy, antidepressants, cognitive behavior, randomized controlled, case–control, clinical trial, intervention, bupropion, methylphenidate, aripiprazole, sertraline, and fluoxetine. Two investigators independently conducted computer and manual searches, with a third expert consulted in the event of disagreement.

Data extraction

Two researchers independently conducted literature screening following the predetermined inclusion and exclusion criteria. The retrieved literature was imported into literature management software and initially screened by title and abstract after duplicate removal. This led to a thorough reading of the complete text and the final decision regarding inclusion. All data were cross-checked and extracted from the included literature and any discrepancies were resolved by consultation with a third party. The data extracted included basic information about the literature, intervention measures, and outcome indicators.

Quality assessment

Two researchers strictly assessed the quality of the included studies according to the Cochrane Handbook for Systematic Reviews of Randomized Trials. Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0. The Cochrane Collaboration). The tool includes seven items: random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective reporting, and other biases. Each item was assessed as "low risk," "unclear risk," and "high risk."

Statistical analysis

The network Meta-analysis package of Stata14.2 was used for network meta-analysis and drawing the network map, and RevMan5.4 was used to evaluate the quality of the included studies. In the statistical process of network meta-analysis, since the outcome measurement indicators of the included studies were continuous data and the scale scoring methods of each study were different, the standard mean difference (SMDs) of different studies and the corresponding 95% confidence intervals (CIs) were used as the effect size to merge the results. First of all, the collected data is tested with the inconsistency model to check whether there is good consistency between the groups and the local areas. If there is consistency, the consistency model is used for further analysis of the data; if the inconsistency is significant, the consistency model cannot be used for subsequent steps. Then, the processed data were sorted using surface under the cumulative ranking curve (SUCRA) values [ 13 ], and all the processing results were summarized in a rank-heat map [ 14 ] to obtain the ranking of the therapeutic effects of various interventions. The SUCRA value refers to the area under the cumulative sequencing probability curve of an intervention, with the value ranging from 0 to 100%. League tables were also drawn to analyze pairwise comparisons between interventions, including SMD values and 95% confidence intervals. Finally, the funnel plot was drawn using Stata 14.2 to identify whether there was a small sample effect.

Literature search

A preliminary search yielded 809 English-language studies and 3012 Chinese-language studies. After reviewing the titles and abstracts, 383 articles remained; following a duplication check and full-text reading, 57 studies were finally included for the network meta-analysis, comprising 63 comparisons. The steps for literature retrieval are shown in Fig. 1 .

Steps of literature retrieval

Characteristics of the included studies

The characteristics of 57 included studies [ 3 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ] are shown in Table 1 , which involved 13 interventions, including psychotherapy/CBT, group psychotherapy, Mindfulness-Based Cognitive Therapy (MBCT), placebo/non-intervention, health education, exercise therapy, electro-acupuncture, drug, EEG biofeedback, rTMS, rTMS + CBT, drug + others, and electro-acupuncture + CBT. A total of 3538 patients with IA were included, and all included studies had comparable descriptions.

Risk of bias assessment (Fig. 2 )

Bias risk assessment for included studies

Two reviewers strictly followed the recommended bias risk assessment tools in the Cochrane Handbook to assess the risk of bias for the included studies. For example, in terms of randomization methods, 22 studies were evaluated as "low risk" as they adopted randomized allocation methods such as random number tables, stratified randomization, and drawing lots. Another 28 studies only mentioned "randomization" without reporting specific randomization methods and were evaluated as "unclear risk." The remaining seven studies randomly assigned patients according to the admission order and were rated "high risk." All studies failed to report whether allocation concealment was performed and was rated as “unclear risk." Seven studies involved blinding and were rated as "low risk." All studies had complete data and were rated as "low risk." Other biases were not mentioned and were rated as "low risk."

Network meta-analysis

Network figure

A total of 57 randomized controlled trials (RCTs) reported the effectiveness of different interventions to treat IA, involving 13 interventions. The size of each node in the network diagram (Fig. 3 ) represents the sample size of the corresponding intervention, and the thickness of the lines that connect different interventions represents the number of studies comparing the two interventions.

Network figure about efficient evidence

Analysis result

Network meta-analysis was performed on the included studies, generating 78 pairwise comparisons with 95% confidence intervals for the SMD. Please refer to Table 2 for detailed information.

The results of the network meta-analysis showed that compared with the placebo/non-intervention group, drug + others (SMD = -2.26, 95% CI = -3.26 ~ -1.26), EEG biofeedback (SMD = -1.62, 95% CI = -2.28 ~ -0.95), rTMS (SMD = -1.22, 95% CI = -1.74 ~ -0.71) showed statistical significance in the treatment effect of IA. Compared to the health education group, drug + others (SMD = -4.25, 95% CI =—6.34 ~ -2.17), rTMS + CBT (SMD = -7.17, 95% CI = -9.82 ~ -4.53), electro-acupuncture + CBT (SMD = -3.24, 95% CI = -4.84 ~ -1.65), EEG biofeedback (SMD = -2.90, 95% CI = -5.73 ~ -0.08), rTMS (SMD = -3.36, 95% CI = -5.56 ~ -1.16), exercise therapy (SMD = -2.71, 95% CI = -4.78 ~ -0.64), group psychotherapy (SMD = -2.32, 95% CI = -4.35 ~ -0.30), psychotherapy/CBT (SMD = -2.40, 95% CI = -3.82 ~ -0.99), and MBCT (SMD = -1.90, 95% CI = -3.05 ~ -0.74) have been shown to be statistically significant in the treatment of IA. Compared to the psychotherapy/CBT group, rTMS + CBT (SMD = -4.77, 95% CI = -7.00 ~ -2.54), electro-acupuncture + CBT (SMD = -0.84, 95% CI = -1.58 ~ -0.10), reflect the difference in therapeutic effect compared to use CBT alone, combined physical therapy is essential for the curative effect. Similarly, drug + others (SMD = -2.12, 95% CI = -3.53 ~ -0.70) also showed statistically different advantages compared to interventions that only used drugs. Furthermore, compared to MBCT, psychotherapy/CBT group, psychotherapy, exercise therapy, electroacupuncture, rTMS, EEG biofeedback, and electroacupuncture + CBT, therapeutic efficacy in the rTMS + CBT group showed optimal differences, and the results were statistically significant ( p < 0.05), which further demonstrating the unique therapeutic effect of rTMS + CBT. This combination of treatment modalities could provide a reference for the treatment of IA in the future.

In terms of efficiency, the ranking was rTMS + CBT > drug + others > rTMS > electro-acupuncture + CBT > EEG-biofeedback > exercise-therapy > psychotherapy/CBT > group-psychotherapy > drug > MBCT > placebo/non-intervention > electro-acupuncture > health-education. The specific rank order is shown in Table 3 , and the cumulative probabilities are shown in Fig. 4 .

Cumulative probabilities

Inconsistency test

The consistency of each closed-loop result was tested. Inconsistency factors (IF) showed p = 0.4042, indicating good consistency. All local p > 0.05, indicating good consistency among all groups.

Publication bias

The research was roughly symmetrically distributed on both sides of the midline, indicating that a small sample effect was less likely to exist as shown in Fig. 5 .

Funnel plot about the 14 interventions in the treatment of IA. A placebo/non-intervention, B health-education, C MBCT, D psychotherapy/CBT, E group-psychotherapy, F exercise-therapy, G electro-acupuncture, H rTMS, I EEG-biofeedback, J electro-acupuncture + CBT, K rTMS + CBT, L drug, M drug + others

IA causes serious physical and mental distress and potential harm to people. Many scholars have investigated prevention and intervention measures for IA in recent years, resulting in various interventions. There have been relatively few studies on treating IA, and the effects of most treatments are limited. To investigate the efficacy, advantages, and disadvantages of different treatment methods used alone or in combination, this study conducted a network meta-analysis of the efficacy of 13 intervention methods. The ranking results showed that the top four intervention measures in terms of effectiveness were rTMS combined with CBT, drug combination with other treatments, rTMS, and electro-acupuncture combined with CBT. The rankings of various comprehensive treatments were also high, indicating that combined therapy can effectively improve the effect of IA compared to using a single intervention measure for treatment.

rTMS combined with CBT

Based on the efficacy ranking, both rTMS combined with CBT and rTMS alone achieved optimal treatment effects. Compared with all other interventions except for drug-combined comprehensive treatment, rTMS combined with CBT has shown statistical differences. rTMS is a physical therapy that generates a sequence of repetitive electromagnetic pulses through an electromagnetic coil. It can regulate cortical excitability by acting on specific cortical regions of the brain [ 71 ]. Numerous domestic and foreign studies have shown that rTMS has immense potential to treat substance dependence [ 72 ]. Studies have shown that rTMS treatment targeting the left or right dorsolateral prefrontal cortex (DLPFC) can fully mobilize the cognitive regulatory capacity of the DLPFC, increase the excitability of cortical areas, and regulate activity by maintaining functional levels of dopamine and other neurotransmitters in various structures of the reward circuit [ 73 ]. Thus, people's cravings for the Internet will be reduced, and their addictive behavior will be curtailed. Furthermore, neuroimaging studies have also found that rTMS applied to DLPFC can effectively inhibit brain cortices related to addictive behavior and sensation, reduce the craving of participants with IA, improve their cognitive control and emotional regulation abilities, reward and cognitive control systems, and thereby reduce the craving and behavior in IA [ 74 , 75 ]. IA can significantly reduce the white matter integrity of DLPFC compared to normal individuals [ 76 ], similar to the changes observed in drug addiction. Excessive use of the internet can alter the reward and pleasure centers of the brain, making it difficult to quit addiction. rTMS can help regulate brain activity and reduce cravings related to IA.

Simultaneously, CBT is also a major intervention method for IA in psychotherapy, which is effective in many randomized controlled studies on IA [ 77 , 78 ]. The essence of CBT is to correct the cognitive dysfunction of patients with IA. Improving cognitive control ability may be the key to solving IA [ 79 ]. The results of the network meta-analysis demonstrated that rTMS combined with CBT treatment was more effective than rTMS or CBT alone. When people try to reduce or withdraw from online activities, IA can lead to withdrawal symptoms. Physical therapy interventions can help control these physiological and emotional withdrawal symptoms, making it easier for patients to receive psychological treatment. Therefore, people's desire for the internet will decrease and their addictive behavior will be restricted [ 80 ]. This could be attributed to the potential of combined treatment to simultaneously improve the physiological, psychological, and behavioral aspects of IA, thereby producing a cumulative effect for better outcomes.

Combination of medication and other therapies

Medication combined with other intervention measures, as a comprehensive intervention measure, demonstrates excellent efficacy, outperforming single medication therapy. Many experts believe that IA indicates impulse control disorders over the internet. This addictive behavior is classified as compulsive behavior [ 81 ]. Researchers suggest that selective serotonin reuptake inhibitors (SSRIs), as first-line drugs for treating obsessive–compulsive disorder, may also have optimal therapeutic effects on patients with IA. The results of our meta-analysis indicated that the efficacy of single drug therapy is only superior to that of the general intervention group, the placebo group, and the single electro-acupuncture group and is comparable to that of a single psychological treatment. However, when combined with psychological or physical treatment, it demonstrates excellent results and is statistically significant compared to single-medication therapy. This may result from medication therapy controlling anxiety and depression to a certain extent in IA patients. For IA patients with poor self-control and resistance to treatment, applying medication first to stabilize the patient's emotions and then using psychological or physical treatment can further improve their depressive and anxious symptoms and cognitive function, thereby reducing their craving for the internet. Additionally, combined therapy can reduce the adverse effects of the long-term use of single medications and improve treatment safety, resulting in better outcomes.

Electro-acupuncture and CBT combined treatment

We also noticed that the combination of electro-acupuncture and CBT treatment had achieved unexpected results. Traditional Chinese medicine has a long history of application in mental illnesses, such as using electro-acupuncture to treat depression and sleep disorders. Studies have demonstrated that electro-acupuncture can promote the recovery of neurons in the affected brain area [ 82 ]. The acupoints used in acupuncture can increase blood flow or induce electrical potentials in specific brain regions [ 83 ]. As a commonly used treatment in traditional Chinese medicine, Electro-acupuncture therapy has gained recognition in treating modern addictive behaviors. The combination of electro-acupuncture and CBT treatment was shown to significantly reduce the addiction level and related clinical symptoms of IGD patients. fMRI is increasingly being used to study the mechanism of acupuncture. Previous studies have confirmed that acupuncture and moxibustion can regulate the structure and function of the brain regions of drug addicts and can regulate the functional connection between the reward and habit systems of IA [ 84 ]. Therefore, the regulatory effect of acupuncture on the brain area of network addiction patients may be a potential mechanism of acupuncture in-network addiction. Both electro-acupuncture and cognitive-behavioral therapy have significant positive effects on network-addicted adolescents. Both treatment methods effectively improve network addiction patients' psychological experience and behavioral expression. However, electro-acupuncture is more effective than psychological therapy regarding impulse control and neuron protection. This advantage may be related to increased NAA and CHO levels in the prefrontal cortex and anterior cingulate cortex [ 82 ]. The combination of electro-acupuncture and psychological interventions (cognitive-behavioral therapy, group, psychological therapy combined with individual psychological therapy) can alleviate the mental symptoms, sleep quality, and impulse characteristics of network addiction patients [ 85 ]. Its mechanism may be related to increased brain sensory perception gate function. Psychological intervention can help relieve the fear of electro-acupuncture and improve the treatment effect, as acupuncture is an exogenous stimulation that usually accompanies pain. Combining CBT with electro-acupuncture treatment has shown superior results compared to using acupuncture alone, suggesting a synergistic effect between electro-acupuncture and CBT. In summary, combining electro-acupuncture and CBT can achieve optimal therapeutic outcomes.

Other interventions

Compared to comprehensive treatment, we have observed that the following five interventions, namely health education, electro-acupuncture, place/non-intervention, MBCT, and drug treatment, have relatively lower effectiveness. We believe this may be attributed to the following reasons. Health education often focuses on encouragement, comfort, and providing knowledge related to IA, but it fails to address the deep-rooted inner struggles of patients and alleviate their dependency on the internet. Therefore, health education is generally less effective in most cases. As for electroacupuncture treatment, studies have shown that IA patients often experience strong withdrawal reactions during the initial stages of treatment, such as restlessness, palpitations, and irritability. As an acupuncture treatment, electroacupuncture may cause swelling and heaviness at the treatment site, and discomfort may persist for a period of time after the needles are removed and gradually subside. Consequently, the compliance and expectations of most patients are not high, resulting in mediocre therapeutic effects of using electroacupuncture alone.

MBCT, as a mindfulness and meditation-oriented treatment method, has been shown in research to reduce craving for IA by improving the understanding ability of IA patients, improving loneliness in IA, and reducing heart rate and cortisol levels [ 23 ]. However, due to the impulsive personality traits of most addiction patients, many individuals are unable to calm their minds and fully engage in the understanding and experience provided by MBCT therapy. Therefore, relying solely on MBCT treatment is ineffective.

The use of drugs alone also fails to achieve better therapeutic effects, mainly because in simple applications of drug therapy, common SSRIs such as sertraline, which selectively inhibit the reuptake of serotonin by central neurons, leading to an increase in serotonin concentration, have slow-acting effects and are associated with side effects such as gastrointestinal discomfort. Consequently, patients have low long-term medication tolerance and poor treatment effectiveness. Moreover, the cognitive function of patients is still insufficient in medication-only treatment [ 3 ]. On the other hand, combination therapy and comprehensive treatment can reduce the adverse reactions caused by long-term medication use, enhance treatment safety, and improve treatment effectiveness. Therefore, a single treatment approach may not be able to address all the issues, and personalized comprehensive treatment plans should be developed based on the unique characteristics of each patient during clinical treatment to ensure compliance and treatment effectiveness.

IA is a complex multifactorial disorder with numerous physiological, psychological, and social elements. A single treatment plan can have difficulty addressing all issues, while a comprehensive therapy can integrate the advantages and disadvantages of various treatments and take targeted measures. Although the findings of this study confirm the safety and feasibility of electro-acupuncture + CBT therapy for IA, there are still some limitations. Due to time constraints, the long-term efficacy of the subjects has not been observed, and the effectiveness assessment was based on clinical scales. Therefore, it is possible that subjects may have concealed some information during the measurement.

(1) Most of the studies were conducted by Chinese researchers. (2) The literature on rTMS combined with CBT treatment and drug combinations with other treatments included in this study was limited.

The results of our study show that, although all treatments were slightly more effective than placebo/non-intervention and health education in treating IA patients, rTMS + CBT had the best therapeutic effect in treating IA patients with different interventions, followed by drugs combined with other treatments, followed by rTMS and electro-acupuncture + CBT. This proves the unique role physical therapy, specifically rTMS therapy, plays in treating patients with IA. Comprehensive intervention can achieve better therapeutic effects than using drugs or psychotherapy alone by combining drug therapy, physical therapy, and psychotherapy. Comprehensive intervention improves the physical, psychological, and behavioral aspects of patients with IA by combining the benefits of various methods.

Availability of data and materials

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

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Acknowledgements

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Provincial Education Department Natural Science Key Project (grant number: 2022AH051512), Shanghai Key Laboratory of Psychotic Disorders Open Grant (grant number: 13dz2260500), Bengbu Medical College key Laboratory of Addiction Medicine (29–3).

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School of Mental Health, Bengbu Medical College, Bengbu, Anhui, 233030, China

Yuqiong Zhu, Junda Li & Wenjuan Wang

School of Second Clinical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, 310053, China

Haihan Chen

School of Qian Xuesen College, Xi’an Jiaotong University, Xi’an, Shanxi, 710049, China

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Zhu, Y., Chen, H., Li, J. et al. Effects of different interventions on internet addiction: a systematic review and network meta-analysis. BMC Psychiatry 23 , 921 (2023). https://doi.org/10.1186/s12888-023-05400-9

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Internet addiction: a systematic review of epidemiological research for the last decade

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1 Doctoral Researcher, International Gaming Research Unit. Nottingham Trent University, NG1 4BU Nottingham, UK. [email protected].
PMID: 24001297
DOI: 10.2174/13816128113199990617

In the last decade, Internet usage has grown tremendously on a global scale. The increasing popularity and frequency of Internet use has led to an increasing number of reports highlighting the potential negative consequences of overuse. Over the last decade, research into Internet addiction has proliferated. This paper reviews the existing 68 epidemiological studies of Internet addiction that (i) contain quantitative empirical data, (ii) have been published after 2000, (iii) include an analysis relating to Internet addiction, (iv) include a minimum of 1000 participants, and (v) provide a full-text article published in English using the database Web of Science. Assessment tools and conceptualisations, prevalence, and associated factors in adolescents and adults are scrutinised. The results reveal the following. First, no gold standard of Internet addiction classification exists as 21 different assessment instruments have been identified. They adopt official criteria for substance use disorders or pathological gambling, no or few criteria relevant for an addiction diagnosis, time spent online, or resulting problems. Second, reported prevalence rates differ as a consequence of different assessment tools and cut-offs, ranging from 0.8% in Italy to 26.7% in Hong Kong. Third, Internet addiction is associated with a number of sociodemographic, Internet use, and psychosocial factors, as well as comorbid symptoms and disorder in adolescents and adults. The results indicate that a number of core symptoms (i.e., compulsive use, negative outcomes and salience) appear relevant for diagnosis, which assimilates Internet addiction and other addictive disorders and also differentiates them, implying a conceptualisation as syndrome with similar etiology and components, but different expressions of addictions. Limitations include the exclusion of studies with smaller sample sizes and studies focusing on specific online behaviours. Conclusively, there is a need for nosological precision so that ultimately those in need can be helped by translating the scientific evidence established in the context of Internet addiction into actual clinical practice.

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Published: 19 March 2024

Relationship between loneliness and internet addiction: a meta-analysis

Yue Wang 1 &
Youlai Zeng 1

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

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

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

Conclusions

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

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Introduction

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

The concept of loneliness

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

The concept of internet addiction

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

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

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

The relationship between loneliness and internet addiction

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

Loneliness leading to internet addiction

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

Internet addiction leading to loneliness

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

A vicious cycle of loneliness and internet addiction

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

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

The moderating variables of the relationship between loneliness and internet addiction

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

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

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

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

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

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

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

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

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

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

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

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

Measurement tool

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

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

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

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

Research design

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

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

Research year

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

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

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

Eligibility criteria

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

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

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

Literature selection criteria

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

Search strategy

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

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

Document coding

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

Data analysis

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

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

Sample characteristics

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

Homogeneity test

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

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

Assessment of publication bias

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

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

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

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

Main effect test

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

Forest plot of the comprehensive effects of loneliness and Internet addiction

Moderating effect test

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

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

Relationship between loneliness and internet addiction

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

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

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

Moderating effect of the relationship between loneliness and internet addiction

The moderating role of gender.

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

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

The moderating role of age

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

The moderating role of grade

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

The moderating role of region

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

The moderating role of measurement tool

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

The moderating role of research design

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

The moderating role of research year

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

Limitations

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

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

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

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

Data availability

Data can be requested from the corresponding author.

Abbreviations

Revised Chen Internet Addiction Scale

Diagnostic and Statistical Manual of Mental Disorders—Fourth Edition

Internet Addiction Disorder

Internet Addiction Test

Population, Intervention, Comparison(s) and Outcome

Pathological Internet Use

Young’s Diagnostic Questionnaire for Internet addiction

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Study of internet addiction and its association with depression and insomnia in university students

Jain, Akhilesh 1 ; Sharma, Rekha 2 ; Gaur, Kusum Lata 3 ; Yadav, Neelam 4 ; Sharma, Poonam 5 ; Sharma, Nikita 5 ; Khan, Nazish 5 ; Kumawat, Priyanka 5 ; Jain, Garima 4 ; Maanju, Mukesh 1 ; Sinha, Kartik Mohan 6 ; Yadav, Kuldeep S. 1,

1 Department of Psychiatry, ESIC Model Hospital, Jaipur, Rajasthan, India

2 Department of Ophthalmology, ESIC Model Hospital, Jaipur, Rajasthan, India

3 Department of PSM, SMS Medical College, Jaipur, Rajasthan, India

4 Department of Medicine, ESIC Model Hospital, Jaipur, Rajasthan, India

5 Department of Psychology, University of Rajasthan, Jaipur, Rajasthan, India

6 Manipal University, Jaipur, Rajasthan, India

Address for correspondence: Dr. Kuldeep S. Yadav, Senior Resident, Department of Psychiatry, ESIC Model Hospital, 3-D/162, Chitrakoot, Ajmer Road, Jaipur - 302 021, Rajasthan, India. E-mail: [email protected]

Received December 18, 2019

Received in revised form January 31, 2020

Accepted February 12, 2020

Introduction:

Use of internet has increased exponentially worldwide with prevalence of internet addiction ranging from 1.6% to 18 % or even higher. Depression and insomnia has been linked with internet addiction and overuse in several studies.

Aims and Objectives:

Present study has looked in to pattern and prevalence of internet addiction in university students. This study has also explored the association of internet addiction with depression and insomnia.

Material and Methods:

In this cross sectional study 954 subjects were enrolled who had been using internet for past 6 months. Information regarding pattern of use and socio demographic characteristics were recorded. Internet addiction Test (IAT), PHQ-9,and insomnia Severity Index (ISI) were applied to measure internet addiction, depression and insomnia respectively.

Results:

Among 954 subjects, 518 (60.59%) were male and 376 (39.41%) were female with mean age of 23.81 (SD ± 3.72). 15.51% study subjects were internet addicts and 49.19% were over users. Several parameters including graduation level, time spent per day on line, place of internet use, smoking and alcohol had significant association with internet addiction. Internet addiction was predominantly associated with depression and insomnia.

Conclusion:

Internet addiction is a rising concern among youth. Several parameters including gender, time spent on line, alcohol, smoking predicts higher risk of internet addiction. Depression and insomnia are more common in internet addicts and overusers.

Introduction

Exponential growth in internet use has been observed across the world including India in the last decade. About 205 million internet users were reported in India in 2012 including both rural and urban population and it was predicted that India will become the second leading country after China in internet usage.[ 1 ] Internet is used for various reasons such as interpersonal communication, exploring information, business transactions, and entertainment. However, it can also provide an opportunity to engage in excessive chatting, pornography, gaming, or even gambling. There have been growing concerns worldwide for what has been labeled as “internet addiction.”

Dr. Ivan Goldberg suggested the term “internet addiction” in 1995 for pathological compulsive internet use.[ 2 ] Excessive internet use was closely linked to pathological gambling by Young[ 3 ] who adapted the DSM IV criteria to relate to internet use in the internet addiction test (IAT) developed by her. The prevalence of internet addiction has been reported ranging from 1.6% to 18% in different populations.[ 4 5 ]

General population surveys show a prevalence of 0.3–0.7%.[ 6 ] with addicted spending average 38.5 h/week on a computer as compared to the nonaddicted averaged 4.9 h/week. Goel[ 7 ] has reported 24.8% as possible addicts, and 0.7% as addicts in his study of internet addiction among Indian adolescents.

Overuse of the internet has been linked with many psychological conditions including anxiety, depression, and insomnia. Several studies[ 8 9 ] have shown that among users addicted to the internet, depression has much prevalence than normal users. Akini and Iskender[ 10 ] have reported that depression and anxiety are significant predictors of internet addiction in a study among Turkish students.

There is an influence of problematic internet use or internet addiction on sleep patterns. Increased time spent on the internet may disrupt the sleep-wake schedule significantly, and a higher rate of sleep disturbance takes place among heavy internet users.[ 11 ] Wong[ 12 ] studied the impact of online addiction on insomnia and depression on Hong Kong adolescents. The findings showed that “internet addiction was associated significantly with insomnia and depression”. These data imply that possible complex mechanisms exist between insomnia, internet addiction, and depression.

Internet use has been overwhelmingly increasing in India, involving especially the youth population. Since adolescents contribute a significant proportion of the productive life age of our country, their involvement with internet overuse or addiction may lead to significant adverse consequences such as sleep disturbance, psychological and physical problems leading to academic decline. Although many studies have been conducted regarding internet addiction in India, nevertheless, not much has been studied in the state of Rajasthan in this regard. Hence, the present study was planned to investigate the pattern and prevalence of internet usage in young adults and its relationship with insomnia and depression in college-going youth.

Material and Method

This cross-sectional study included 1000 students of both sexes using the internet for the past 6 months from different streams in the University of Rajasthan and affiliated colleges. Formula n = z 2 × pq/d 2 was used to determine the study sample size where n represents a total number of sample, z corresponds to value at 95% confidence interval, P stands for the prevalence of internet addiction in the previous study i.e. almost 44 percent,[ 13 ] and d represents allowable error. Thus, a sample size of about 600 students was considered appropriate. This study included 1000 students out of which 46 students opted out in the middle of the study, hence 954 students finally constituted the study sample. The study participants were selected using simple random sampling. Approval was obtained from the concerned authority. Participants were informed about the nature and purpose of the study before including them in the study.

Only those participants constituted the study group who had been using the internet for the past 6 months and were willing to participate in the study. Those who did not choose to participate, having major medical or surgical problems, history of psychosis or mania, MDD, or any other mental disorder were excluded from the study.

Information was collected on a specially designed semi-structured performa containing details of demographics, educational qualification, and status, purpose of using the internet (by choosing among the options like education, entertainment, social networking or other purpose), money spent per month, place of access (home, cybercafé, or workplace if working part-time), the time of day when the internet is accessed the most (by choosing between morning, afternoon, evening, or night), and the average duration of use per day.

Internet addiction, depression, and insomnia were assessed on IAT, patient health questionnaire (PHQ-9), and insomnia severity index (ISI), respectively.

Internet addiction test (IAT)

The IAT[ 3 ] is a 20-item 5-point Likert scale that measures the severity of self-reported compulsive use of the internet. According to Young's criteria, total IAT scores 20–39 represent average users with complete control of their internet use, scores 40–69 represent over-users with frequent problems caused by their internet use, and scores 70–100 represent the internet addicts with significant problems caused by their internet use.

Patient health questionnaire (PHQ-9)

A self-report version of PRIME-MD11 which assesses the presence of major depressive disorder using modified diagnostic and statistical manual, fourth edition (DSM-IV) criteria.[ 14 ] In this study, the Hindi version of PHQ-9 was used. It has been validated in the Indian population and is considered to be a reliable tool for the diagnosis of depression. For the diagnosis of depression, we define clinical significant depression as a PHQ-9 score of 8–9 as minor depression, a PHQ-9 score of 10 or greater as moderate depression; a score of 15 or more and one of the two cardinal symptoms (either depressed mood or anhedonia) as definite major depression. We considered PHQ 9 score of 10 or more as depression in this study.

Insomnia severity index (ISI)

ISI is one of the most commonly used disease-specific measures for self-perceived insomnia severity. The ISI has 7 items describing insomnia-related health impairments.[ 15 ] Each item is rated on a 5-point Likert scale. In clinical assessments, the ISI total summary score falls into 1 of 4 ISI categories; with scores 0–7, 8–14, 15–21, and 22–28 indicating no clinically significant insomnia, sub-threshold insomnia, moderate insomnia and, clinically severe insomnia, respectively.

We used the Hindi version of the ISI[ 16 ] Clinically, significant insomnia was detected only when the ISI score was >14.

Statistical analysis

All data collected were entered into the Microsoft excel 2007 worksheet in the form of a master chart. These data were classified and analyzed as per the aims and objectives. The data on sample characteristics were described in the form of tables. Categorical variables were tabulated using frequencies and percentages. Inferential statistics such as the Chi-square test were used to find out the association of internet usage with various factors. Odds ratio (OR) was used to find out the association of insomnia and PHQ levels with internet usage.

This study was a cross-sectional questionnaire-based study conducted among university students from different faculties.

Around 954 subjects with age ranging from 17 to 34 years were included in this study. The mean age was 23.81 years with a 3.72 standard deviation. Males were 578 (60.59%) and females were 376 (39.41%). Out of total subjects, 412 (43.19%) were internet over users and 148 (15.51%) were addicts [ Table 1 ].

Out of 954 subjects, 537 were postgraduates while 417 were undergraduates [ Table 2 ]. Among postgraduates (PG) 96 (17.88%) had internet addiction and 241 (44.88%) were over users, whereas the proportion of internet addicts and over users among undergraduates (UG) was 12.47% and 41.01%, respectively [ Table 3 ]. This association was statistically significant. Similarly, on application of Chi-square test, significant association was found between place of internet usage and addiction as 68 (25.19%) subjects were addicted and 128 (47.41%) were over users among those who were using internet at workplace as compared to 141 (16.51%) addicts and 360 (47.41%) over users among those using it at home. In this study, 86 subjects were smokers and 88 were alcoholics and the association of these personal habits and internet usage was also significant on the application of the Chi-square test. Out of them 86 smokers, 27 (31.40%) were addicted and 34 (39.53%) were over users and out of 88 alcoholics, 10 (11.36%) were addicted and 45 (51.14%) were over users. Considering duration of internet usage, it was observed that those using internet for more than 2 h a day were more addicted and over users [ 85 (19.81%) and 188 (43.82%), respectively] as compared to those using internet for less than 2 h a day [ 63 (12%) and 224 (42.67%), respectively] and this association was found significant. A total of 437 (45.81%) subjects reported insomnia among whom 107 ((24.49%) were internet addicts and 241 (55.15%) were over users whereas those subjects without insomnia comprised only 47 (7.93%) addicts and 171 (33.08%) over users. This association was again statistically significant. Similar results were noted with regard to the presence of depression. Depression was reported in 421 (44.13%) subjects including 113 (26.84%) internet addicts and 225 (53.44%) over users. Among those subjects without depression number of internet addicts and over users was 35 (6.58%) and 187 (34.96%), respectively. This observation was statistically significant.

On the application of OR considering internet usage as exposure, it was observed that chances of insomnia were more than five times on the internet over users as compared to average users [OR = 5.62 (4.20 to 7.53)]. Similarly, the estimated risk of PHQ ≥10 was observed more than five times on the internet over users as compared to average users. [OR = 5.70 (4.25 to 7.67]) [ Table 4 ].

The present study is an attempt to understand the pattern of internet use and the prevalence of internet addiction in youth college students. The mean age of the study population was 23.8 years.

Our study showed 15.5% of students with internet addiction. Wide variations ranging from 1.6% to 18% in the prevalence of internet addiction among adolescents have been reported.[ 4 ]

Prevalence of PIU (problematic internet use) among adolescents in a multicentric study in Europe was reported ranging from 1.2% to 11.8%.[ 5 ]

Another study on Indian adolescents reported the prevalence of possible addicts and addicts as 24.8% and 0.7%, respectively.[ 7 ] Similarly Kawabe et al .[ 17 ] in his study among 853 adolescents in Japan determined the prevalence of internet addiction using IAT. The prevalence of possible addicts and addicts was 21.7% and 2.0%, respectively.

In the present study boys were more internet addicts and over user than the girls. Similar observations have been made in several other studies in the past.[ 18 ] Since boys are given more liberty in our society and have more frequent access to use the internet in private than the girls predisposing them to become an addict and over the user. Studies have also shown that boys tend to play more online games and surf adult sites more often than girls.[ 19 ] Male also tend to use more addictive substances than female and it has also been reported in a meta-analysis of internet addiction that a person with a history of addiction to other substances is at higher risk of internet addiction.[ 20 ]

Internet addiction and overuse were more prevalent in postgraduate students than undergraduates. Kwabe et al .[ 17 ] has also reported that the number of students with internet addiction increases as their grades increase. Likewise, XinM.[ 21 ] in his study of 6468 Chinese adolescents also observed more internet addiction among older grade students. It is possible that the study course in PG is more demanding to access the internet and the affordability in this group to bear the expenses of the internet is much stronger than UG. It was also evident in general that educational activity was the most commonly cited purpose for internet use.

The workplace was the most preferred area for internet use among addicts. Goel et al .[ 7 ] in his study has also yielded similar results. Lesser restriction, a company of colleagues, and accessibility to free internet may have been the possible reason for this observation.

Alcohol consumption and smoking were significantly associated with internet addiction in the present study. Several other studies in the past have made similar observations where pathological internet use (PIU) or internet addiction was possibly associated with alcohol and smocking.[ 22 ]

Sung et al .[ 23 ] in his analysis of a large study sample of adolescents established a positive association of internet addiction with alcohol and smoking. Neuropsychological explanation proposes that nicotine and alcohol shares a common reward pathway.[ 24 ] which may also include the nature of internet usage as observed in many studies for e.g. striatal activation in online games when confronted with cues from their favorite games.[ 25 ] Studies have also suggested that adolescents with internet addiction may have personalities vulnerable to any other addiction and hence are at increased risk of substance abuse.[ 26 ]

In this study mean daily time spent on internet use was positively correlated with internet addiction. In a review of research on internet addiction, people at risk of internet addiction were reported to have spent significantly more time online.[ 27 ] In another study by Kuss[ 19 ] daily use of the internet and increased time online was positively correlated with internet addiction.

This study revealed a strong positive association between internet addiction and insomnia. Similar results were also established by Bhandari et al .[ 28 ] in his study of 984 undergraduate students, who reported 35.4% of the study sample having poor sleep quality and internet addiction as well. Cheung[ 29 ] in his study of 719 Chinese adolescents in Hong Kong observed high comorbidity between internet addiction and insomnia; 51.7% of students among internet addicts were insomniacs.

Association between internet addiction and depression in this study corresponds to another study among university students in Turkey by Orsala et al .[ 30 ] who reported an alarming association between internet addiction and depression. A high score of depression has also been reported in a study among Indian adolescents with internet addiction.[ 7 31 32 ] In an article on association between internet addiction and depression, the authors after taking into account several studies proposed four models of such association including escape model, bidirectional model, negative consequence model, and shared mechanism model. Grossly the association between internet addiction and psychological problems including depression are reported to have an interdependent relationship. Depression may lead to internet addiction and vice versa. Internet use in this population serves as a remedy to overcome their problems and negative perception about the situation. Such use over a period of time becomes a habit and eventually addiction, as some positive emotions like happiness and excitement are felt while being on the internet. When internet addict does not use the internet, negative emotions flare up and can only be replaced by positive emotions by using the internet.[ 8 ]

The association between internet addiction, insomnia, and depression was explored in a study that observed that internet addiction and sleep quality independently mediated 16.5% and 30.9% indirect effect of each other on depression.[ 28 ]

Internet addiction among youth has become increasingly a great concern. Various parameters including gender, time spent on the internet, graduation level, place of internet use, smoking, and alcohol have been associated with internet addiction. In addition, insomnia and depression are more common in internet addicts and may have a bidirectional relationship.

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“Internet Addiction”: a Conceptual Minefield

Francesca c. ryding.

Department of Psychology, Edge Hill University, St Helens Road, Ormskirk, Lancashire L39 4QP UK

Linda K. Kaye

With Internet connectivity and technological advancement increasing dramatically in recent years, “Internet addiction” (IA) is emerging as a global concern. However, the use of the term ‘addiction’ has been considered controversial, with debate surfacing as to whether IA merits classification as a psychiatric disorder as its own entity, or whether IA occurs in relation to specific online activities through manifestation of other underlying disorders. Additionally, the changing landscape of Internet mobility and the contextual variations Internet access can hold has further implications towards its conceptualisation and measurement. Without official recognition and agreement on the concept of IA, this can lead to difficulties in efficacy of diagnosis and treatment. This paper therefore provides a critical commentary on the numerous issues of the concept of “Internet addiction”, with implications for the efficacy of its measurement and diagnosticity.

What Is Internet Addiction (IA)?

Traditionally, the term addiction has been associated with psychoactive substances such as alcohol and tobacco; however, behaviours including the use of the Internet have more recently been identified as being addictive (Sim et al. 2012 ). The concept of IA is generally characterised as an impulse disorder by which an individual experiences intense preoccupation with using the Internet, difficulty managing time on the Internet, becoming irritated if disturbed whilst online, and decreased social interaction in the real world (Tikhonov and Bogoslovskii 2015 ). These features were initially proposed by Young ( 1998 ) based on the criteria for pathological gambling (Yellowlees and Marks 2007 ), and have since been adapted for consideration within the DSM-5. This has been well received by many working in the field of addiction (Király et al. 2015 ; Petry et al. 2014 ), and has been suggested to enable a degree of standardisation in the assessment and identification of IA (King and Delfabbro 2014 ). However, there is still debate and controversy surrounding this concept, in which researchers acknowledge much conceptual disparity and the need for further work to fully understand IA and its constituent disorders (Griffiths et al. 2014 ).

Much of the debate relates to the issue that IA is conceptualised as addiction to the Internet as a singular entity, although it incorporates an array of potential activities (Van Rooij and Prause 2014 ). That is, the Internet, in all its formats, whether accessed via PC, console, laptop or mobile device, is fundamentally a portal through which we access activities and services. Internet connectivity thus provides us with ways of accessing the following types of activities; play (e.g. online forms of gaming, gambling), work (accessing online resources, downloading software, emailing, website hosting), socialising (social networking sites, group chats, online dating), entertainment (film databases, porn, music), consumables (groceries, clothes), as well as many other activities and services. In this way, the Internet is a highly multidimensional and diverse environment which affords a multitude of experiences as a product of the specific virtual domain. Thus, it is questionable as to whether there is any degree of consistency in the concept of IA, in light of these diverse and specific affordances which may relate to Internet engagement. Indeed, it has been indicated that there are several distinct types of IA, including online gaming, social media, and online shopping (Kuss et al. 2013 ), and it has been claimed that through engagement in these behaviours, individuals may become addicted to these experiences, as opposed to the medium itself (Widyanto et al. 2011 ). Thus, IA is arguably too generalised as a concept to adequately capture these nuances. That is, an individual who spends excessive time online for shopping is qualitatively different from someone who watches or downloads porn excessively. These represent distinct behaviours which are arguably underpinned by different gratifications. Thus, the functionality of aspects of the Internet is a key consideration for research in this area (Tokunaga 2016 ). This is perhaps best approached from a uses and gratifications perspective (LaRose et al. 2003 ; Larose et al. 2001a ; Wegmann et al. 2015 ), to more fully understand the aetiology of IA (discussed subsequently). This is often best underpinned by the uses and gratifications theory (Larose et al. 2001a , 2003 ), which seeks to explain (media) behaviours by understanding their specific functions and how they gratify certain needs. Indeed, in the context of IA, this may be particularly useful to establish the extent to which certain Internet-based behaviours may be more or less functional in need gratification than others, and the extent to which it is Internet platform itself which is driving usage or indeed the constituent domains which it affords. If the former, then controlling Internet-based usage behaviour more generically is perhaps appropriate, however, a more specified approach may often be required given the diverse needs the online environment can afford users.

IA from a Gratifications Perspective

It is questionable on the extent to which IA is itself the “addiction” or whether its aetiology relates to other pre-existing conditions, which may be gratified through Internet domains (Caplan 2002 ). One particular theory that has been referenced throughout much developing research (King et al. 2012 ; Laier and Brand 2014 ) is the cognitive-behavioural model, proposed by Davis ( 2001 ). This model suggests that maladaptive cognitions precede the behavioural symptoms of IA (Davis 2001 ; Taymur et al. 2016 ). Since much research focuses on the comorbidity between IA and psychopathology (Orsal et al. 2013 ), this is particularly useful in underpinning the concept of IA, and perhaps provides support that IA is a manifestation of underlying disorders, due to its psychopathological aetiology (Taymur et al. 2016 ). Additionally, the cognitive-behavioural model also distinguishes between both specific and generalised pathological Internet use, in comparison to global Internet behaviours that would not otherwise exist outside of the Internet, such as surfing the web (Shaw and Black 2008 ). As such, it would assume those individuals who spend excessive time playing poker online, for example, are perhaps better categorised as problematic gamblers rather than as Internet addicts (Griffiths 1996 ). This has been particularly advantageous in the contribution to defining IA, as earlier literature tended to focus solely on either content-specific IA, or the amount of time spent online, rather than focussing as to why individuals are actually online (Caplan 2002 ). Indeed, this shows promise in resolving some of the aforementioned issues in the specificity of IA, as well as the likelihood of pre-existing conditions underpinning problematic behaviours on the Internet.

Much of the recent literature in the realm of IA has focused upon Internet Gaming Disorder (IGD) which has recently been included as an appendix as “a condition for further study” in the DSM-5 (American Psychiatric Association 2013 ). This has driven a wide range of research which has sought to establish the validity of IGD as an independent clinical condition (Kuss et al. 2017 ). Among the wealth of research papers surrounding this phenomenon, there remains large disparity within the academic community. Although some researchers claim there is consensus on IGD as a valid clinical disorder (Petry et al. 2014 ), others do not support this (e.g. Griffiths et al. 2016 ). As such, the academic literature has some way to go before more established claims can be made towards IGD as a valid construct, and indeed how this impacts upon clinical treatment.

One means by which researchers could move forward in this regard is to establish the validity of IGD to a wider range of gaming formats. That is, IGD research has predominantly defined the reference point in studies as “online games” or in some cases, is has been even less specific (Lemmens et al. 2015 ; Rehbein et al. 2015 ; Thomas and Martin 2010 ). Arguably, there are a range of forms of “online” gaming, including social networking site (SNS) games which are Internet-mediated and thus by definition, would appear under the remit of IGD. Indeed, links between SNS and gaming have been previously noted (Kuss and Griffiths 2017 ), although this has not specifically been empirically explored in the context of IGD symptomology. For example, causal form of gaming as is typically the case for SNS gaming have their own affordances in respect of where and how they are played, given these are often played on mobile devices rather than on more traditional PC or console platforms. Further, the demographics of who are most likely to play these games can vary from others forms of gaming which have predominated the IGD literature (Hull et al. 2013 ; Leaver and Wilson 2016 ). Accordingly, these affordances present additional nuances, which the literature has not yet fully accounted for in its exploration of IGD. Clearly, IGD relates to a specific form of Internet behaviour which may be conceptualised within IA, yet is paramount to understand it as a separate entity to ensure the conceptualisation and any associated treatment provision is sufficiently nuanced. Likewise, the same case can be made for many other Internet-based behaviours which may be best being established in respect of their functionality and gratification purposes for users.

IA as a Contextual Phenomenon

There is growing evidence suggesting that context is key towards the processes and cognitions associated with consumption of substances such as alcohol (Monk and Heim 2013 , 2014 ; Monk et al. 2016 ), highlighting some important implications towards understanding IA, as a form of behavioural addiction. That is, the study of IA has rarely been studied in respect of its contextual affordances, even though the combination of Internet connectivity (WiFi) and mobility (smartphones) means that the Internet may be accessed in many ways and in multiple contexts. It has been indicated by Griffiths ( 2000 ) that few studies consider the context of Internet use, despite many users spending a substantial amount of time on the Internet via the use of different platforms, such as mobile devices, as opposed to a computer (Hadlington 2015 ). It has been highlighted by Kawabe et al. ( 2016 ) that smartphone ownership in particular is rapidly increasing, and for some, smartphone devices have become a substitute for the computer (Aljomaa et al. 2016 ). It has also been suggested that the duration of usage on smartphones have been significantly associated with IA (Kawabe et al. 2016 ). This can largely be attributed to the advancement of smartphone technology, which permit them to function as a “one-stop-shop” for a variety of our everyday needs (checking the time, replying to emails, listening to music, interacting with others, playing games), and thus it is understandable that we are spending more of our time in using these devices. This further implicates research in IA, as this has often focussed on users’ Internet engagement through computers as opposed to mobile devices, albeit the numerous Internet subtypes accessible through mobile devices (Sinkkonen et al. 2014 ). One Internet subtype in particular which may facilitate addictive behaviours are social networking sites such as Facebook (Wu et al. 2013 ). Particularly, research has identified a positive relationship between daily usage of smartphones and addictive symptoms towards Facebook (Wu et al. 2013 ). This may also be the case for behaviours such as gaming through SNS which are typically accessed on mobile devices rather than computers. However, of critical interest here, is that addiction to these games has been argued to fall under the classification of IGD, despite being online via Facebook (Ryan et al. 2014 ). This indicates that the platform of Internet access is important in online behaviours, as well as implicating that further distinction between Internet subtypes should be made (particularly within SNS), to establish the different features of these, and how these affordances may be related to excessive usage. This issue is particularly pertinent given the increased interest in “smartphone addiction” (Kwon et al. 2013 ) in which the name assumes we are simply studying addiction to our smartphones themselves, not necessarily the functions they are affording to us. Research such as this is assuming the “problem” is the interaction with the technology (e.g. specific device) itself, when this is most likely not the case. Indeed, recent evidence highlights that different uses/functions of smartphones may be more likely to prompt users to feel more “attached” to the device than others, and that usage is often framed by one’s current context (Fullwood et al. 2017 ).

In addition to being able to access the Internet through multiple platforms, we are often reliant on the Internet for many everyday tasks, which poses a further issue in conceptualising what is “problematic” compared to “required” usage. The increased exposure to the Internet in both work and education make it difficult to avoid usage in such environments (Kiliҫer and Ҫoklar 2015 ; Uçak 2007 ), and it could be argued that the amount of time spent on the Internet for such contexts cannot be reflected as an addiction (LaRose et al. 2003 ). This is pertinent in light of much research, which tends to rely on metrics such as time spent online (e.g. average hours per week) as a variable in research paradigms. Particularly, this tends to be used to correlate against other psychological factors, such as depression or well-being, to indicate how “internet use” may be a problematic predictor of these outcomes (e.g. Sanders et al. 2000 ). In light of the aforementioned issues, this does not offer any degree of specificity in how time spent online is theoretically related to the outcomes variables of interest (Kardefelt-Winther 2014 ). Other studies have approached this with greater nuance by considering specific activities, such as number of emails sent and received in a given time period (Ford and Ford 2009 ; LaRose et al. 2001b ), or studied Internet use for a variety of different purposes, such as for health purposes and communication (Bessière et al. 2010 ). Further, other researchers have highlighted the distinction between behaviours such as smartphone “usage” versus “checking” (Andrews et al. 2015 ), whereby the latter may represent a more compulsive and less consciously driven and potentially more addictive form of behaviour than actual “usage”. These more nuanced approaches provide a more useful and theoretically insightful means of establishing how time spent online may be psychologically relevant as a concept. This suggests that future research which theorises on the impacts of “time spent online” (or “screen-time use”) should provide distinction between usage for work/education and leisure, and the gratification this engagement affords, to obtain greater nuance beyond the typical flawed metrics such as general time spent online.

A further compliment to the existing IA literature would be greater use of behavioural measures which garner users’ actual Internet-based behaviours. This is particularly relevant when considering that almost all existing research on smartphone addiction or problematic use, for example has been based on users’ self-reported usage, with no psychometric measure being validated against behavioural metrics. Worryingly, it has been noted that smartphone users grossly underestimate the amount of times they check their smartphone on a daily basis, with digital traces of their smartphone behaviours illuminating largely disparate findings (Andrews et al. 2015 ). Clearly, there is much opportunity to establish forms of Internet usage by capitalising on behavioural metrics and digital traces rather than relying on self-report which may not always be entirely accurate.

The concept of IA is more complex than it often theorised. Although there have been multiple attempts to define the characteristics of IA, there a numerous factors which require greater clarity in the theoretical underpinnings of this concept. Specifically, IA is often considered from the perspective that the Internet itself (and indeed the technology through which we access it) is harmful, with little specificity in how this functions in different ways for individual users, as well as the varying affordances which can be gained through it. Unfortunately, this aligns somewhat with typical societal conceptions of “technology is harmful” perspective, rather than considering the technology itself is simply a portal through which a psychological need is being served. This perspective is not a new phenomenon. Most new media has been subject to such moral panic and thus this serves a historical tradition within societal conception of new media. Indeed, this has been particularly relevant to violent videogames which scholars have discussed in respect of this issue (Ferguson 2008 ). Whilst many scholars recognise this notion through the application of a user and gratifications perspective, stereotypical conceptions of “technology is harmful” still remain. This raises the question about how we as psychologists can enable a cultural shift in these conceptions, to provide a more critical perspective on such issues. The pertinence of this surrounds two key issues; firstly that moving beyond a “technology is harmful” perspective, particularly for concerns over “Internet addiction” as one example, can enable a more critical insight into the antecedents of problematic behaviour to aid treatment, rather than simply revoking access from the Internet for such individuals. Arguably, this latter strategy would not always address the route of the issue and raises implications about the extent to which recidivism would occur upon reinstating Internet access. Secondly, on a more general level, diverging from an “anti-technology” perspective can enable researchers to draw out the nuances of specific Internet environments and their psychological impacts rather than battling with more blanket assumptions that “technology” (as a unitary concept) is presenting all individuals with the same issues and affordances, regardless of the specific virtual platform or context. In this way, we may be presented with more plentiful opportunities to more critically explore individuals and their interactions across many Internet-mediated domains and contexts.

Compliance with Ethical Standards

Conflict of interest.

The authors declare that they have no conflicts of interest.

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(PDF) A Systematic Review of Literature on Effect of Internet Addiction
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COMMENTS

Internet Addiction: A Brief Summary of Research and Practice
Furthermore, the current work gives a good overview of the current state of research in the field of internet addiction treatment. Despite the limitations stated above this work gives a brief overview of the current state of research on IAD from a practical perspective and can therefore be seen as an important and helpful paper for further ...
Internet addiction in young adults: A meta-analysis and systematic
This meta-analysis shows that the incidence of Internet addiction in adults was high in recent years (2017-2020). The effect size returned according to the random effects model is Z = 24.63; SE = 0.205; p = .001. In addition, high heterogeneity is evident in the research addressing this topic (Q = 1240.719, df = 36, p < .001; I2 = 97.09%).
A study of internet addiction and its effects on mental health: A study
The results of the current study specified that the total mean score of the students for internet addiction and mental health was 3.81 ± 0.88 and 2.56 ± 0.33, correspondingly. The results revealed that internet addiction positively correlated with depression and mental health, which indicated a negative relationship (P > 0.001). The multiple ...
Current Research and Viewpoints on Internet Addiction in Adolescents
In this clincial study, patients with internet addiction and either panic disorder or generalized anxiety disorder received medication for their anxiety and 10 sessions of modified CBT. All 39 patients showed improved anxiety and internet addiction scores reduced on average. [ PMC free article] [ PubMed] 75.••.
Effects of different interventions on internet addiction: a systematic
Globally, Internet is a recognized form of leisure, but there are growing apprehensions about the increasing number of individuals developing an addiction to it. Recent research has focused on social issues associated with internet addiction (IA). However, the treatment of IA is currently unclear. This study aimed to explore the relationship between IA treatment outcomes and different ...
Combatting digital addiction: Current approaches and future directions
This paper presents a narrative review on approaches to combat digital addiction. ... Internet Addiction and Internet Gaming Disorder countermeasure studies were published between the time frame of 2010-2021, whereas 94.1% of countermeasure studies on Smartphone Addiction and all countermeasure studies on Social Media Addiction were published ...
Current Research and Viewpoints on Internet Addiction in Adolescents
Purpose of Review This review describes recent research findings and contemporary viewpoints regarding internet addiction in adolescents including its nomenclature, prevalence, potential determinants, comorbid disorders, and treatment. Recent Findings Prevalence studies show findings that are disparate by location and vary widely by definitions being used. Impulsivity, aggression, and ...
Internet addiction: a systematic review of epidemiological research for
In the last decade, Internet usage has grown tremendously on a global scale. The increasing popularity and frequency of Internet use has led to an increasing number of reports highlighting the potential negative consequences of overuse. Over the last decade, research into Internet addiction has proliferated. This paper reviews the existing 68 ...
Clinical psychology of Internet addiction: a review of its
Introduction. Given the ubiquity of the Internet, its evolving nature as a modern tool of society, and issues surrounding its excessive use and abuse by a minority of people, Internet addiction (IA) has become an increasingly important topic for dedicated research agendas from several scientific fields including psychology, psychiatry, and neuroscience.
Identifying Internet addiction profiles among adolescents using latent
Research paper. Identifying Internet addiction profiles among adolescents using latent profile analysis: Relations to aggression, depression, and anxiety ... Research suggests that Internet addiction is a significant influencing factor for adolescent mental health (Cerniglia et al., 2017; Lam, 2014).
Using Theoretical Models of Problematic Internet Use to Inform
Empirical research has been produced on the topic of 'Internet Addiction' or 'Problematic Internet Use' (PIU) for more than 20 years, with a variety of theoretical approaches suggested by scholars to account for the behaviour. However, the discourse has been fraught with debate around construct definition, measurement, and validity.
Internet addiction and problematic Internet use: A systematic review of
INTRODUCTION. Over the last 15 years, the number of Internet users has increased by 1000%[], and at the same time, research on addictive Internet use has proliferated.Internet addiction has not yet been understood very well, and research on its etiology and natural history is still in its infancy[].Currently, it is estimated that between 0.8% of young individuals in Italy[] and 8.8% of Chinese ...
Relationship between loneliness and internet addiction: a meta-analysis
In the digital age, the Internet has become integrated into all aspects of people's work, study, entertainment, and other activities, leading to a dramatic increase in the frequency of Internet use. However, excessive Internet use has negative effects on the body, psychology, and many other aspects. This study aims to systematically analyze the research findings on the relationship between ...
PDF Current Research and Viewpoints on Internet Addiction in ...
Abstract. Purpose of Review This review describes recent research findings and contemporary viewpoints regarding internet addiction in adolescents including its nomenclature, prevalence, potential determinants, comorbid disorders, and treatment. Recent Findings Prevalence studies show findings that are disparate by location and vary widely by ...
Study of internet addiction and its association with depress ...
The association between internet addiction, insomnia, and depression was explored in a study that observed that internet addiction and sleep quality independently mediated 16.5% and 30.9% indirect effect of each other on depression. Conclusion. Internet addiction among youth has become increasingly a great concern.
PDF Internet Addiction and Identity: A Systematic Research Review
ademics despite thelack of current literature in this. field. Most research studies (89%) focus on young individualsaged nine to 30 years. ld, while. n offline identity was prov. n in a number of studies based onparticipants' statements. Mor. identity studies are addressed in 30% while internet addiction and identity are addressed in.
PDF Full research paper INTERNET ADDICTION IN UNIVERSITY STUDENTS
oned findings, in our research, we focused on students of Czech universities. The purpose of the presented research is to detect the prevalence of Internet addiction among university students in the Czech Republic and its risk factors, including gender, age, the form of studies (full-. ime vs. part-time), the type of university studied and ...
Association of internet addiction with depression, anxiety, stress, and
Participants filled out paper-and-pen questionnaires in a group after their university lecture. All participants reported daily Internet use both via smartphones and desktop computers. ... A conceptual and methodological critique of internet addiction research: towards a model of compensatory internet use. Comput. Hum. Behav., 31 (2014), pp ...
Internet Addiction and its Relationships with Depression, Anxiety, and
The finding of higher prevalence of addiction in female respondents was in contrast with other studies conducted by Mazalin and Moore 2004, Chen and Fu 2009, and Hasanzadeh et al. 2012 on a teenage population on the association between gender and Internet addiction, where male Internet addiction was found to be significantly higher. This could ...
Internet Addiction: The Problem and Treatment
This paper reviews the existing 68 epidemiological studies of Internet addiction that (i) contain quantitative empirical data, (ii) have been published after 2000, (iii) include an analysis ...
A study on Internet addiction and its relation to psychopathology and
The Internet is used to facilitate research and to seek information for interpersonal communication and for business transactions. On the other hand, it can be used by some to indulge in pornography, excessive gaming, chatting for long hours, and even gambling. ... The Internet Addiction Test has been found to be the only validated instrument ...
Prevalence and Factors Associated with Problematic Internet Use among
Search for more papers by this author. Desalegn Tarekegn, Desalegn Tarekegn. ... which was used to measure the severity of anxiety symptoms and is still widely used today in both clinical and research settings. ... Students classified as moderate and severe Internet addiction were considered problematic Internet use users (scores from 50 to 100 ...
"Internet Addiction": a Conceptual Minefield
IA from a Gratifications Perspective. It is questionable on the extent to which IA is itself the "addiction" or whether its aetiology relates to other pre-existing conditions, which may be gratified through Internet domains (Caplan 2002).One particular theory that has been referenced throughout much developing research (King et al. 2012; Laier and Brand 2014) is the cognitive-behavioural ...
Prevalence and determinants of Internet Addiction among medical
Research paper. Prevalence and determinants of Internet Addiction among medical students and its association with depression. ... Internet Addiction is an important psychological problem affecting about 9 % of Assiut university medical students during their undergraduate stage, which may interfere with their lives and studies. ...

Effects of different interventions on internet addiction: a systematic review and network meta-analysis

Materials and methods

Search strategy

Data extraction

Quality assessment

Statistical analysis

Literature search

Characteristics of the included studies

Risk of bias assessment (Fig. 2 )

Network figure

Analysis result

Inconsistency test

Publication bias

rTMS combined with CBT

Combination of medication and other therapies

Electro-acupuncture and CBT combined treatment

Other interventions

Availability of data and materials

Acknowledgements

Author information

Authors and Affiliations

Contributions

Corresponding author

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Internet addiction: a systematic review of epidemiological research for the last decade

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Relationship between loneliness and internet addiction: a meta-analysis

Conclusions

Introduction

The concept of loneliness

The concept of internet addiction

The relationship between loneliness and internet addiction

Loneliness leading to internet addiction

Internet addiction leading to loneliness

A vicious cycle of loneliness and internet addiction

The moderating variables of the relationship between loneliness and internet addiction

Measurement tool

Research design

Research year

Eligibility criteria

Literature selection criteria

Search strategy

Document coding

Data analysis

Sample characteristics

Homogeneity test

Assessment of publication bias

Main effect test

Moderating effect test

Relationship between loneliness and internet addiction

Moderating effect of the relationship between loneliness and internet addiction

The moderating role of age

The moderating role of grade

The moderating role of region

The moderating role of measurement tool

The moderating role of research design

The moderating role of research year

Limitations

Data availability

Abbreviations

Acknowledgements

Author information

Contributions

Corresponding author

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Consent for publication

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