internet addiction research introduction

ORIGINAL RESEARCH article

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

$\r\nLorenzo Zamboni,*$

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

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

Introduction

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

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

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

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

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

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

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

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

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

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

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

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

Materials and Methods

Participants.

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

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

Instruments

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

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

Internet Addiction Test (IAT; Young, 1998 )

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

Socio-Demographics

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

Control Variables

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

Statistical Analyses

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

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

Preliminary Analyses

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

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

Measurement Model

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

Moderation Analysis

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

Table 2. Results of the moderation analysis.

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

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

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

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

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

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

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

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

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

Data Availability Statement

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

Ethics Statement

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

Author Contributions

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

Conflict of Interest

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

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

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

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

Reviewed by:

Copyright © 2020 Zamboni, Portoghese, Congiu, Carli, Munari, Federico, Centoni, Rizzini and Lugoboni. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Lorenzo Zamboni, [email protected]

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

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Internet addiction: definition, assessment, epidemiology and clinical management

Affiliation.

1 Department of Psychiatry, University of Iowa Roy J. and Lucille A. Carver College of Medicine, Iowa City, Iowa 52242, USA.
PMID: 18399706
DOI: 10.2165/00023210-200822050-00001

Internet addiction is characterized by excessive or poorly controlled preoccupations, urges or behaviours regarding computer use and internet access that lead to impairment or distress. The condition has attracted increasing attention in the popular media and among researchers, and this attention has paralleled the growth in computer (and Internet) access. Prevalence estimates vary widely, although a recent random telephone survey of the general US population reported an estimate of 0.3-0.7%. The disorder occurs worldwide, but mainly in countries where computer access and technology are widespread. Clinical samples and a majority of relevant surveys report a male preponderance. Onset is reported to occur in the late 20s or early 30s age group, and there is often a lag of a decade or more from initial to problematic computer usage. Internet addiction has been associated with dimensionally measured depression and indicators of social isolation. Psychiatric co-morbidity is common, particularly mood, anxiety, impulse control and substance use disorders. Aetiology is unknown, but probably involves psychological, neurobiological and cultural factors. There are no evidence-based treatments for internet addiction. Cognitive behavioural approaches may be helpful. There is no proven role for psychotropic medication. Marital and family therapy may help in selected cases, and online self-help books and tapes are available. Lastly, a self-imposed ban on computer use and Internet access may be necessary in some cases.

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Current Research and Viewpoints on Internet Addiction in Adolescents

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

Cite this article

internet addiction research introduction

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

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Purpose of Review

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

Recent Findings

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

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

Which Are the Optimal Screening Tools for Internet Use Disorder in Children and Adolescents? A Systematic Review of Psychometric Evidence

Internet Gaming Disorder and Addictive Behaviors Online

Behavioral Addictions: Some Developmental Considerations

Avoid common mistakes on your manuscript.

Introduction

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

Defining the Issue

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

A Focus on Gaming

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

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

Scales and Assessment

Building effective igd scales.

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

Internet Addiction Scales

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

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

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

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

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

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

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

Potential Determinants of PIMU

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

Family Functioning

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

Personality Traits

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

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

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

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

Neurobiology and Brain Function

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

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

Comorbidities

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

Depression and Anxiety

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

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

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

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

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

Cognitive Behavioral Therapy

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

Pharmacological Treatment

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

Group and Family Therapies

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

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

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

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

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Bickham, D.S. Current Research and Viewpoints on Internet Addiction in Adolescents. Curr Pediatr Rep 9 , 1–10 (2021). https://doi.org/10.1007/s40124-020-00236-3

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Article publication date: 11 July 2018

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The purpose of this paper is to delineate the overall theoretical framework on the topic of internet addiction through the comprehensive narrative review to make readers aware of the conceptual growth and development in the respective field. The paper evolves theoretically from the historical foundation, phenomenology, clinical feature, etiological model to the treatment outcome of internet addiction. Multiple studies have been done in the field of mental health but dearth of work given head to toe theoretical overview for understanding of this trendsetter research area in mental health.

Design/methodology/approach

Extensive review of literature has been carried out to make a systematic layout for conceptual paper.

The internet has been a source of gratification for several behavioral addictions as well as psychiatric disorders. Mainly because of the lack of established diagnostic criteria and a dearth of large sample surveys, the prevalence of problematic internet use (PIU) in general population has not been established. Still, from all the consolidated data, PIU seems to have a male preponderance and manifests itself in late adulthood. Symptoms of PIU can easily be masked with signs of dependence, tolerance and withdrawal which is quite similar to the phenomenology of substance addiction. Psychiatric co-morbidities are more of a norm than the exception in case of PIU. Even though the clinical status of PIU is doubtful, still there is a significant demand for its treatment all over the world. Overall, the excessive use of internet has been strongly debated in literature from PIU to a positive addiction. Only time will tell how it affects our civilization as a phenomenon of evolutionary significance.

Originality/value

The paper is providing a general conceptual framework for internet addiction/PIU to enable readers to know about the topic in depth from the evolution of the concept to the recent developments in the area.

Substance use disorder
Internet addiction
Behavioural addiction
Problematic internet use (PIU)
Psychiatric co-morbidity

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

Bisen, S.S. and Deshpande, Y.M. (2018), "Understanding internet addiction: a comprehensive review", Mental Health Review Journal , Vol. 23 No. 3, pp. 165-184. https://doi.org/10.1108/MHRJ-07-2017-0023

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What to Do If You're Addicted

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

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

Top 5 Things to Know About Internet Addiction

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

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

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

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

Excessive Use of the Internet

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

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

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

How Often Do You...

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

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

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

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

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

Negative Repercussions

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

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

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

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

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

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

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

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

What to Do If You Have an Internet Addiction

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

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

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

Dresp-Langley B, Hutt A. Digital addiction and sleep . IJERPH . 2022;19(11):6910. doi:10.3390/ijerph19116910

American Psychiatric Association. Internet Gaming .

Young KS, de Abreu CN. Internet Addiction: A Handbook and Guide to Evaluation and Treatment . New York: John Wiley & Sons Inc.; 2011.

Holoyda B, Landess J, Sorrentino R, Friedman SH. Trouble at teens' fingertips: Youth sexting and the law . Behav Sci Law . 2018;36(2):170-181. doi:10.1002/bsl.2335

Jorgenson AG, Hsiao RC, Yen CF. Internet Addiction and Other Behavioral Addictions . Child Adolesc Psychiatr Clin N Am . 2016;25(3):509-520. doi:10.1016/j.chc.2016.03.004

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

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

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

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

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

Alavi SS, Ferdosi M, Jannatifard F, Eslami M, Alaghemandan H, Setare M. Behavioral Addiction versus Substance Addiction: Correspondence of Psychiatric and Psychological Views . Int J Prev Med . 2012;3(4):290-294.

American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, DSM-5. 5th ed. Washington, DC: American Psychiatric Association Publishing; 2013.

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

Internet Addiction

Overview of internet addiction.

Internet addiction is defined as an unhealthy behavior that interferes with and causes stress in one’s personal, school, and/or work life. Like other addictions, compulsive Internet usage completely dictates a person’s life. Addicts struggle to control their behavior, causing a sense of despair, leading them to dive further in their addictive pattern. After some time, addicts become dependent on cyberspace to feel normal.

Signs & Symptoms

Some of the signs and symptoms are lack of sleep and excess fatigue; withdrawal from campus and social activities and events; declining grades; lying about how much time is spent online and what they do there; and general apathy, edginess, or irritability when off-line.

The best prevention is education about the difference between Internet use and abuse and to raise awareness. One should also have a good understanding of the importance of social interaction. Ultimately, face-to-face contact contributes to a sense of psychological security and happiness. Lastly a person should have a general knowledge of the social activities and events the campus has to offer.

Abstinence from the Internet is not recommended for Internet addicts because the use of the Internet is sometimes required for ones work or school life. For some people treatment may involve learning time management skills, setting goals, using reminder cards, or developing a personal inventory. Others may want to seek counseling and support groups or family therapy.

SHCS Counseling Services
Center for Online Addiction (eBehavior, LLC)
Center for Online Addiction (HealthyPlace Addictions Community)

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v.2021; 2021

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

Farzaneh noroozi.

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

Soheil Hassanipour

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

Fatemeh Eftekharian

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

Kumars Eisapareh

Mohammad hossein kaveh.

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

Associated Data

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

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

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

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

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

1. Introduction

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

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

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

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

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

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

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

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

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

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

2. Materials and Methods

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

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

2.1. Data Sources and Search Strategy

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

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

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

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

2.2. Inclusion Criteria

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

2.3. Exclusion Criteria

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

2.4. Study Selection

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

2.5. Articles' Quality Assessment

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

2.6. Data Extraction and Quality Assessment

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

2.7. Statistical Analysis

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

3.1. Search Results

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

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Flowchart of the included studies in systematic review.

3.2. Articles' Quality Assessment

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

3.3. Characteristics of Included Studies

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

Data extraction results from studies.

Author/year	Country	Study population	Age mean (SD)	Sample size	QOL instrument	IA instrument
Fatehi et al. (2016)	Iran	7–4-year medical students	22.57 ± 1.2	174	WHOQOL-BREF	IAT
Li et al. (2018)	China	High school students	15.1 ± 1.9	1385	WHOQOL-BREF	IAT
Chern et al. (2018)	Taiwan	Students	20.51 ± 1.8	1452	HRQOL	IAT
Gupta et al. (2016)	India	Adolescent (18–23 years)	—	60	WHOQOL-BREF	IAT
Geisel et al. (2015)	USA, UK, Canada	Adult social network gamers	38:9 ± 13.4	370	WHOQOL-BREF	IAT
Kamal Solati (2018)	Iran	Students of Islamic Azad university	—	381	WHOQOL-BREF	IAT
Li et al. (2020)	China	University students	20.3 ± 1.6	2312	WHOQOL-BREF	The mobile phone addiction scale (MPAS)
Kelley and Gruber (2013)	USA	Undergraduate students (18 to 39 years old)	19.6 ± 2.96	133	SF-36v2 health survey	Problematic internet use questionnaire (PIUQ)
Gupta et al. (2018)	India	Adolescent (18–23 years old)	—	23	WHOQOL-BREF	IAT
Gao et al. (2020)	Germany	College students and highly educated adults.	25.8 ± 11.6	446	WHOQOL	ISS-10 (short version of the ISS-20)
Tabak and Zawadzka (2017)	Polish	Students	16.04 ± 0.9	376	KIDSCREEN-10 index	YDQ (8 items)
Tran et al. (2017)	Vietnamese	Young (15–25 years old)	21.5 ± 3.8	566	EuroQol	IAT
Tran et al. (2017)	Vietnamese	young (15–25 years old)	21.7 ± 1.7	586	EuroQol	IAT
Buctot et al. (2020)	Filipino	Adolescents (13–18 years old)	15.22 ± 1.61	1447	KIDSCREEN-27	Smartphone addiction scale short version (SAS-SV)
Gao et al. (2017)	Chine	University students	20.50 ± 1.4	722	WHOQOL-BREF	Mobile phone addiction scale (MPAS)
Paolo Soraci et al. (2020)	Italian	Online survey via Google forms	33.8 ± 16.2 18–99 years	205	Quality of life measure	Smartphone application based addiction scale (SABAS)
Karacic et al. (2017)	Germany	Students of primary and high school	11–18 years	149	SF-36	I IAT
Silvana Karacic et al. (2017)	Croatian	Students of primary and high school	11–18 years	310	SF-36	I IAT

3.4. Statistical Analysis

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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Funnel plot for assessing possible publication bias.

4. Discussion

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

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

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

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

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

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

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

4.1. Strengths and Limitations

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

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

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

5. Conclusion

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

Acknowledgments

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

Abbreviations

IA:	Internet addiction
QOL:	Quality of life
OR:	Odds ratio
HRQOL:	Health-related quality of life
PRISMA:	Preferred reporting items for systematic reviews and meta-analysis
WHO:	World Health Organization.

Data Availability

Conflicts of interest.

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

Supplementary Materials

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

Open access
Published: 19 August 2024

Reliability and validity of the Chinese version of the doomscrolling scale and the mediating role of doomscrolling in the bidirectional relationship between insomnia and depression

Lu Yang 1 , 2 ,
Xuejiao Tan 3 ,
Rui Lang 4 ,
Tao Wang 1 , 2 &
Kuiliang Li 3 , 5

BMC Psychiatry volume 24 , Article number: 565 ( 2024 ) Cite this article

Metrics details

Doomscrolling behavior is very common among college students. The purpose of this study was to evaluate the reliability and validity of the Chinese version of the Doomscrolling Scale, thus providing a scientific basis for its application among Chinese university students.

The Chinese version of Doomscrolling Scale was developed through translation and revision of the original scale, conducting item and factor analysis, and validating it with validation factor analysis. The psychometric properties of the Doomscrolling Scale were assessed in 2885 Chinese university students.

The internal consistency coefficients, two-month test-retest reliability, and split-half reliability of the Chinese version of the Doomscrolling Scale (including the 15-item and the 4-item short version) were high, and the mono-factorial scales fitted well to the theoretical model. Scores on the Chinese version of the Doomscrolling Scale were significantly associated with depression, anxiety, and smartphone addiction. The structural equation model indicates that doomscrolling can mediate the bidirectional relationship between insomnia disorder and depression.

Conclusions

The revised Chinese version of the Doomscrolling Scale is valid and reliable, which can facilitate research in this field. The association between doomscrolling and various mental disorders has been confirmed, and further research should be conducted to investigate its mechanisms of action.

Peer Review reports

Introduction

The term “doomscrolling” first emerged in 2018 and was popularized by journalist Karen Ho [ 1 ]. It refers to a behavior observed in individuals who engage in continuous scrolling through social media news, fixating on distressing, depressing, or other negative information [ 2 ]. The act of consistently exposing oneself to negative news on social media and news feeds has been conceptualized as “doomscrolling”. This behavior is often defined as a habit, characterized by compulsively scrolling through social media and news updates, with users becoming obsessed with seeking out disheartening negative information [ 3 ]. Sharma et al. defined “doomscrolling” as the habitual and immersive scanning of timely negative information in social media news feeds [ 4 ]. From the aforementioned conceptualization, it is apparent that doomscrolling leads to a vicious cycle that drives individuals to compulsively consume negative information. Regardless of the severity of these negative stimuli, individuals eventually fall into an endless pattern of seeking negative information, consequently impacting their mental and physical health [ 5 , 6 ].

In an era where social media news is ubiquitous, excessive consumption of negative information can lead to psychological and behavioral issues such as depression, anxiety, and addiction. Prior research conducted on 747 social media users revealed that engaging in doomscrolling produces a sense of addictive excitement [ 7 ], which is posited to be a primary driver of doomscrolling behavior. In terms of negative consequences, studies have found that high frequency of doomscrolling is associated with greater severity of depression, increased anxiety about the future, reduced psychological well-being, and lower life satisfaction [ 7 ]. Research has shown that consuming only 15 min of a news program can increase state anxiety and mood dysregulation in college students [ 8 ]. Furthermore, individuals who engage with higher-intensity negative news are more likely to exhibit maladaptive behaviors [ 7 ]. Specifically, doomscrolling is associated with higher impulsivity, greater stimulus-seeking behavior, and lower motivation to avoid unhealthy behaviors. Additionally, consuming negative news similarly has an impact on individual behavior. Specifically, consuming negative news is linked to negative emotions, reduced prosocial behavior [ 9 ], decreased prosocial intentions [ 10 ], and lower self-control [ 4 ].

A significant external factor contributing to the development of doomscrolling is the personalized information delivery implemented by various news media and social platforms based on user preferences (i.e., The more negative information one reads, the more negative content related apps will push) [ 11 ]. This undoubtedly amplifies the exposure to negative information for individuals. Therefore, screening for negative information consumption holds critical significance in the prevention and intervention of adverse behaviors and psychological issues.

Sharma and colleagues developed a self-report measurement tool for doomscrolling [ 4 ], aiding researchers in exploring the relationships between doomscrolling and other psychological health variables. This unidimensional scale comprises 15 items (e.g., “I feel an impulse to seek bad news on social media, and it is becoming more frequent”), scored on a 7-point Likert scale, where 1 indicates “strongly disagree” and 7 indicates “strongly agree”. The sum of all items’ scores reflects the extent of an individual’s inclination toward consuming negative information, offering an accessible and comprehensible measurement approach.

Doomscrolling is a relatively novel concept that has been studied by only a few researchers. For instance, the reliability and validity of the Doomscrolling Scale were validated among individuals in Turkey [ 5 ]. However, there is currently a lack of research on negative information consumption and effective measurement tools in China. Compared to primary and secondary school students, college students in China have more leisure time and a higher level of autonomous consumption capability. Consequently, they tend to use smartphones more extensively [ 12 ], potentially increasing their exposure to negative information through social media feeds. Therefore, this study aims to revise the Doomscrolling Scale and examine its applicability among Chinese university students, thereby providing a reliable tool for investigating negative information consumption. In addition, because of the relationship between doomscrolling and insomnia and depression, we also explored the mediating role of doomscrolling in the bidirectional relationship between insomnia and depression through structural equation modeling.

The research focused on the validation of a doomscrolling questionnaire among the Chinese population. Based on the original scale, item analysis, exploratory and confirmatory factor analysis, and reliability analysis were conducted for both the 15-item and 4-item short versions of the questionnaire. Correlation analysis with the criterion-related questionnaire was also performed.

Ethical statement

The study procedure was submitted to the Ethics Committee of the Chongqing Key Laboratory of Psychological Diagnosis and Educational Technology for Children with Special Needs for ethical approval. The submitted materials included the research design, questionnaire content, data collection methods, informed consent forms, participant recruitment procedures, and potential risk assessments. The Ethics Committee evaluated the ethicality, safety, and scientific validity of the study and eventually granted ethics approval (Ethics Number: CSTJ-RE-20230620004). Additionally, the study was conducted anonymously, and the data were securely stored by designated personnel to fully protect the privacy of the participants and enhance the reliability of the results. As a token of appreciation, participants were entered into a draw with a 1/10 chance of winning 1 RMB upon completing the questionnaire.

Survey procedure and participants

On June 26 and 27, 2023, an initial survey was conducted among college students from two tertiary institutions in Chongqing, China using a convenient sampling method. A total of 2938 college students participated in this survey. The survey questionnaire was presented and data were collected through quick response (QR) codes generated by the Wenjuanxing platform ( www.wjx.cn ). The survey was administered by the mental health centers of the university. The survey QR codes were distributed to class advisors by the research team, who then forwarded them to class WeChat groups. Participants scanned the QR codes to take part in the survey. Prior to beginning the survey, participants were presented with the purpose of the study and provided informed consent. Choosing to answer the questionnaire implied their consent to participate. The inclusion criteria were: (1) college or university students currently enrolled in a specialized or undergraduate program; (2) an age of 18 years or older; (3) being capable of using internet tools such as mobile phones or computers. The study did not set explicit exclusion criteria, but during data analysis, responses from participants who took too short a time to complete the questionnaire were excluded.

The survey was conducted online, and all questions were set as mandatory, minimizing the occurrence of missing data. For the sake of survey reliability, participants who completed the questionnaire in less than 120 s were excluded, given that completing the questionnaire within such a short timeframe might lead to careless responses. The time threshold was determined based on the number of items and the average fastest completion time by researchers familiar with the questionnaire content. Additionally, we ensured that the proportion of excluded data was less than 5% of the total data, which is considered not to affect the sample’s representativeness [ 13 ]. Additionally, since age was an open-ended question, participants who provided incorrect information were also excluded. Finally, 53 participants (1.80%) were excluded, resulting in a final analysis with data from 2885 college students, with 598 participants in Sample 1 and 2287 participants in Sample 2. The effective response rate was 98.2%. The participants ranged in age from 18 to 23 (M = 19.36, SD = 0.93), and 1726 of them (59.83%) were male.

On August 24, 2023, a retest was conducted using the same procedures and methods as the initial test, involving 1251 participants. A total of 1251 sets of data were collected in Sample 3. A total of 48 questionnaires (3.84%) were excluded from the analysis due to a low response time of less than 50 s for 24 items. The specific exclusion criteria were consistent with that of the initial test. Ultimately, 1203 questionnaires were included for analysis, with an effective response rate of 96.16%. The average age of the participants was 19.46 (SD = 0.88), and 607 of them were male.

Measurement tools

Doomscrolling scale.

The Doomscrolling Scale, developed by Sharma et al. in 2022, was employed for assessment [ 4 ]. This scale comprises 15 items (e.g., “I feel an urge to seek bad news on social media, more and more often”), rated on a 7-point Likert scale, where 1 indicates “strongly disagree” and 7 indicates “strongly agree”. Higher scores on the questionnaire indicate a stronger inclination toward doomscrolling. The internal consistency coefficients for the original study and this study were 0.960 and 0.982, respectively. Two experts in psychology and English, and one expert in public management and English, were invited to translate the Doomscrolling Scale, producing the initial Chinese draft. Subsequently, the Chinese version was back-translated into English and checked by native English speakers, revealing no significant discrepancies. In this way, the Chinese version of Doomscrolling Scale was developed. For a more comprehensive understanding of the translation process, please refer to the supplementary materials.

Depressive symptoms

The Patient Health Questionnaire (PHQ-9), based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) diagnostic criteria, consists of 9 items [ 14 ]. This self-report questionnaire employs a four-point Likert scale ranging from 0 to 3 for each item, with a total score between 0 and 27. A higher total score corresponds to a more severe level of depression. The internal consistency coefficients for the original study and this study were 0.890 and 0.904, respectively.

Anxiety symptoms

Generalized Anxiety Disorder (GAD-7) Scale for Measuring Anxiety Levels in university students [ 15 ]. The GAD-7 consists of 7 items and is a self-report questionnaire. Each item is rated on a Likert four-point scale ranging from 0 to 3, with a total score ranging from 0 to 21. A higher total score indicates a higher level of anxiety. The internal consistency coefficients for the original study and this study were 0.920 and 0.939, respectively.

Smartphone addiction questionnaire

A simplified version of the Smartphone Addiction Scale (SAS-SV) was employed for measuring smartphone usage [ 16 ]. The SAS-SV is a unidimensional self-report questionnaire comprising 10 items. It employs a 6-point Likert scale (1 indicating “strongly disagree” and 6 indicating “strongly agree”). The internal consistency coefficients for the original study and this study were 0.967 and 0.932, respectively.

Data analysis

We performed statistical analyses using SPSS version 26.0 and Amos version 21.0. We conducted a series of exploratory factor analyses (EFA), a multivariate statistical analysis method used to explore potential relationships between variables. Factor analysis reveals the intrinsic structure among variables and simplifies the data by reducing its dimensionality [ 17 ]. Confirmatory factor analysis (CFA) was used to test whether the current data fits the structural model from the EFA and the original scale. CFA assesses the reliability of the theoretical model by comparing the fit between the observed data and the theoretical model [ 18 ]. CFA was conducted using maximum likelihood estimation, and the dimensions were derived from EFA (with only one dimension) from AMOS Graphics. The structural validity of the Chinese version of Doomscrolling Scale was assessed. Indices such as Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Incremental Fit Index (IFI), and Standardized Root Mean Square Residual (SRMR) were used to assess model fit. Fit standards were adopted as follows: RMSEA ≤ 0.08, SRMR ≤ 0.08, CFI ≥ 0.90, TLI ≥ 0.90, and item loadings > 0.60, in accordance with criteria established by Bowman and Goodboy [ 19 ]. If the results of the CFA did not meet these fit standards, revisions were planned for items with substantial residual values. Finally, the revised Chinese version of the 15-item Doomscrolling Scale and the 4-item short version of the Doomscrolling Scale were subjected to correlation analysis with depression, anxiety, and smartphone addiction. The detailed steps of the analysis were as follows.

Initially, reliability testing and item analysis were conducted on the collected sample 1. The critical ratio method, total score correlation method, and reliability tests were employed for item selection. The unidimensional structure of the 15-item and 4-item Doomscrolling Scale were validated using exploratory and validation factor analyses. Then, convergent validity was established with reference to the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and Smartphone Addiction Scale-Short Version (SAS-SV). The reliability of this questionnaire was assessed using Cronbach’s α, McDonald’s omega (ω), test-retest reliability, and split-half reliability.

Table 1 presents the demographic information of the participants. The majority of participants were freshmen and sophomores, accounting for 79.58% and 18.99% of the total, respectively. Over 70% of the participants were from rural areas, and only 15.08% reported urban household registration. The vast majority of participants reported a medium or low economic status, with 62.63% of participants indicating a medium economic status. More than 32.26% of participants used their smartphones for 2–4 h per day, and 58.75% used them for more than 4 h. Nearly half of the participants (45.93%) reported mild or higher levels of depressive symptoms.

Chinese version of the 15-item doomscrolling scale

Correlation analyses of Doomscrolling Scale scores for each item and the total scores showed correlation coefficients ranging from 0.793 to 0.936. Effective items were screened according to the critical ratio (CR). Participants with the top 27% of total scores were grouped as the high-score group, and those with the bottom 27% were grouped as the low-score group. An independent samples t-test was then conducted to identify items that could not effectively differentiate participant responses, and these items were removed to improve the reliability and validity of the scale [ 20 ]. Independent sample t-tests were conducted to compare the scores of these two groups on each item. The results revealed significant differences across all items between the high- and low-score groups ( p < 0.001), indicating good discriminant validity.

The EFA results showed a Kaiser-Meyer-Olkin (KMO) value of 0.964 and a significant Bartlett’s test of sphericity ( p < 0.05), suggesting the suitability of the data for factor analysis. Hence, employing maximum variance rotation, a single common factor was extracted based on eigenvalues > 1, contributing to a cumulative variance of 78.11%. Factor loadings ranged from 0.784 to 0.939, indicating a well-structured scale. Factor loadings, descriptive statistics, and item-total score correlations are presented in Table 2 . CFA was conducted to assess the model fit of the Chinese version of the 15-item Doomscrolling Scale. The items were fit into a single factor, with the model fit indices yielding χ ²/ df = 40.685, RMSEA = 0.132, SRMR = 0.034, CFI = 0.930, TLI = 0.918, and IFI = 0.930. Although there was substantial item intercorrelation, omitting items did not improve model fit. Consequently, modification indices were examined, and 13 residual covariances were added to the model (between items 1 and 2, 1 and 3, 2 and 3, 4 and 5, 5 and 6, 6 and 7, 8 and 9, 9 and 10, 12 and 13, 12 and 15, 13 and 14, 13 and 15, 14 and 15), resulting in improved fit indices: χ ²/ df = 14.911, RMSEA = 0.078, SRMR = 0.019, CFI = 0.979, TLI = 0.971, and IFI = 0.979. All these indices met acceptable model fit criteria.

We made adjustments to some of the paths, item 1: I feel an urge to seek bad news on social media, more and more often, item 2: I lose track of time when I read bad news on social media. Both items explore an individual’s behavioral tendencies and emotional responses on social media, as well as their perception of time spent on social media. However, the two differ in their temporal responses, with item 1 focusing on the frequency of social media use, whereas item 2 focusing on the duration of social media use. Items 1 and 3 exhibit significant covariation, as both pertain to the use of social media push notifications, with item 1 displaying particularly high covariation. But item 1 emphasizes an increase in usage frequency, while item 2 emphasizes the behavior of constantly refreshing; items 6 and 7 both pertain to negative emotions individuals experience online, such as feelings of anxiety and panic, but there are differences in the intensity of the reactions. The other revised paths also have high commonalities in some aspects between each other. Therefore, it is necessary to modify these paths to improve the fit and ensure they align with the data’s structure and relationships. Consistent with the original scale study, the corrected residuals were predominantly from adjacent items, possibly due to the presentation order rather than unincorporated multidimensionality [ 4 ].

The Chinese version of the 15-item Doomscrolling Scale exhibited significant positive correlations with the PHQ-9, GAD-7, and SAS-SV. The total score of the Chinese version of the 15-item Doomscrolling Scale was more strongly correlated with smartphone addiction scores than with depression and anxiety scores (Table 3 ). The revised Chinese version of the 15-item Doomscrolling Scale demonstrated internal consistency (Cronbach’s alpha) of 0.98, McDonald’s omega (ω) of 0.98, test-retest reliability of 0.98, and split-half reliability of 0.94.

Chinese version of the 4-item brief doomscrolling scale

The EFA results for the 4-item Brief Scale showed a KMO value of 0.736 and a significant Bartlett’s test of sphericity ( p < 0.05), confirming suitability for factor analysis. A single common factor was extracted in the factor analysis, contributing to a cumulative variance of 79.11%. All items had factor loadings > 0.6, indicating good structural validity.

CFA was conducted for the brief Doomscrolling Scale. The initial model fit indices did not meet the fit criteria, prompting model modification. The results of the revised model and related fit indices are presented in Table 4 . Given that each item loaded on a single factor, although with substantial contributions, and considering the strong intercorrelation between items, adjustments were made for large residual values between items 1 and 2. The modified model yielded primary fit indices of χ ²/ df = 5.204, RMSEA = 0.043, SRMR = 0.002, CFI = 0.999, TLI = 0.997, and IFI = 0.999, all meeting acceptable model fit criteria. The improved model fit indicated good structural validity.

The 4-item brief Doomscrolling Scale demonstrated significant positive correlations (Table 3 ) with PHQ-9, GAD-7, and SAS-SV. The internal consistency of the short version of the 4-item Doomscrolling Scale was 0.91, the McDonald’s omega (ω) was 0.95, test-retest reliability was 0.97, and split-half reliability was 0.83.

After confirming the validity of the Doomscrolling Scale among the Chinese population, we further explored the mediating role of doomscrolling behavior in the bidirectional relationship between insomnia disorder and depression. This aims to enhance our understanding of the relationship between insomnia disorder and depression.

Participants

Study 2 comprised 578 college students recruited following the same procedure as Study 1. The sample consisted of 361 male students and 217 female students, with a mean age of 19.52 (SD = 1.06) years, ranging from 18 to 23 years. The average score for depression was 4.89 (SD = 4.73), and the average score for insomnia disorder was 0.79 (SD = 0.83).

Measurements

Insomnia disorder was assessed using sleep condition indicators [ 21 ], with the online version of the scale comprising 7 items. Each item is scored on a Likert scale of 0–4 points. Scores ranging from 0 to 2 indicate no clinical diagnosis of insomnia, while scores greater than 2 are considered indicative of clinical insomnia disorder. Higher total scores in this study indicate poorer sleep [ 21 ]. The internal consistency coefficients of the sleep condition indicators in the original study and this study were 0.857 and 0.889, respectively. Assessment of depressive symptoms was conducted using the PHQ-9, with relevant scale information referenced in Study 1.

First, Spearman correlation analysis was used to explore the correlation between insomnia disorder, depression, and doomscrolling. Subsequently, the Mplus 8.3 software was used for structural equation modeling (SEM) and analysis. SEM is a multivariate data analysis method used to explore complex relationships between hypothesized structures and indicators [ 22 ]. This method is typically used to explain hypothesized causal relationships between latent variables. In a structural model, the relationships between variables are usually represented by diagrams with arrows, which indicate the predictive and predicted relationships, and provide the magnitude of these hypothesized effects [ 23 ]. This method is suitable for exploring the bidirectional predictive relationship between depression and insomnia disorders, which may be mediated by doomscrolling behavior. Maximum likelihood estimation and 5000 bootstrap samples were employed for model estimation. Two mediation models were constructed separately with insomnia and depression as independent variables.

The distribution of participants’ demographic information was similar to that in Study 1 (Table 1 ). The correlation analysis revealed a significant positive correlation between doomscrolling and insomnia disorder ( r = − 0.47, p < 0.01), as well as a significant positive correlation with depression ( r = − 0.42, p < 0.01). The insomnia-depression mediation model showed that doomscrolling mediated the relationship between insomnia disorder and depression in a saturated model, with all path coefficients being significant (Fig. 1 a). In the insomnia-depression model, the total effect of insomnia disorder on depressive symptoms was 0.40, with a mediation effect of 0.25, accounting for 62.5% of the total effect. This indicated that 62.5% of the influence of insomnia disorder on depressive symptoms is mediated by doomscrolling (Table 5 ). The mediation model of depression and insomnia is also a saturated model, with all paths being significant (Fig. 1 b). The total effect value of the model was 0.40, with a direct effect from depression to insomnia of 0.21 and a mediation effect of 0.19. The mediation effect accounted for 47.5% of the total effect (Table 5 ).

Mediation models for insomnia disorder and depression indicators via doomscrolling ( a . doomscrolling mediates insomnia and depression; b . doomscrolling mediates depression and insomnia)

Self-reports from news and social media users indicated that doomscrolling was characterized by individuals compulsively showing interest in timely and negative information, often accompanied with anxiety, leading them to be immersed in large volumes of negative news for extended periods [ 3 , 24 ]. Consequently, we believe that excessive attention to and continued consumption of negative news may increase individuals’ fears and uncertainties about the future, exacerbating depression and anxiety among viewers [ 25 , 26 ]. Therefore, it is crucial to investigate populations inclined towards doomscrolling. This study involved the adaptation of the Doomscrolling Scale developed by Sharma et al. into a Chinese version, providing an effective measurement tool tailored to the doomscrolling behavior of Chinese university students, thereby facilitating research on doomscrolling behavior in China.

EFA revealed that the Chinese version of Doomscrolling Scale was unidimensional, consistent with the one-dimensional structure of the original scale [ 4 ]. CFA results indicated that the fit indices RMSEA for both the 15-item and the abbreviated 4-item Chinese versions of the scale were above the recommended standards before modification [ 27 ]. Therefore, a similar approach to previous research was adopted to modify the fit model of the scale, achieving acceptable standards [ 4 ]. One possible reason for the elevated RMSEA values could be that both the revised Chinese version and the original scale were unidimensional. Some studies argued that traditional cutoff values may not necessarily apply to fit standards for single-factor models, as fit indices have different sensitivities to missing cross-loadings and factor covariates in single-factor models. Thus, the appropriateness of using these cutoff values in single-factor models remains uncertain [ 28 ]. Based on other good model fit indices, such as SRMR and CFI, we considered the model fit in the current study to be acceptable, as the RMSEA index was also not considered in the Turkish revision of Doomscrolling Scale [ 5 ].

A comparative analysis with the original study showed that the internal consistency reliability of the Chinese version was 0.98, slightly higher than that of the original scale (0.96), with both versions’ internal consistency coefficients exceeding the preferably acceptable value of 0.8 [ 29 ]. Additionally, the test-retest reliability of the Chinese version was 0.98, which was not mentioned in the original scale but the reliability was similar. In terms of construct validity, the results for RMSEA, SRMR, and CFI indicators are consistent (supplementary material table S1 ), but the chi-square to degrees of freedom ratio (Chinese version: 14.911 vs. original version: 4.167) was higher than that of the original study, likely due to the large sample size in this study [ 30 ]. These findings support the reliability and validity of the Chinese version of Doomscrolling Scale, ensuring that the translated version is equivalent to the original English version and that the findings are reliable across different language contexts.

Correlation analysis revealed a significant correlation between Doomscrolling scores and depression, anxiety, and smartphone addiction. Research has unveiled a relationship between social media usage and increased depressive and anxious emotions [ 31 ], i.e., individuals engaging in more doomscrolling were more likely to experience psychological distress [ 5 ]. Negative news followers exhibited compulsive behavior in consuming adverse information, making them more prone to mental health issues such as depression and anxiety [ 32 ], and this browsing behavior may also be related to smartphone usage. Correlational validity analysis of the Chinese version of the 15-item Doomscrolling Scale and the abbreviated 4-item version showed that their total scores were higher in correlation with smartphone addiction scores than with depression and anxiety scores. This suggests that individuals addicted to smartphones may find it challenging to break free from the compulsion to browse negative information, as addiction can lead to problematic usage of social media platforms and the internet [ 5 ]. However, smartphone addiction is prevalent among Chinese university students [ 33 ], which may increase the risk of doomscrolling behavior among individuals. Research suggests that the widespread availability of highly functional modules in smartphones among Chinese university students is a significant factor contributing to smartphone addiction [ 34 ]. The powerful features of smartphones can inundate users with various types of information, and the push of negative news may be a critical contributing factor for the frequent co-occurrence of depression [ 35 ] and anxiety [ 36 ] among smartphone addicts. This important topic warrants further exploration in the future.

However, despite the potential risks associated with the doomscrolling behavior, there is currently little government attention given to this issue. In China, the use of social media among university students has become quite common [ 37 ], and there are instances of excessive usage of platforms such as Douyin [ 38 ], Weibo [ 39 ], WeChat [ 40 ], and Bilibili. Without exception, these platforms analyze individuals’ browsing preferences and habits to push targeted content [ 11 ], which is one of the external factors leading to their excessive use of media software. However, this has garnered little attention from relevant authorities. We believe that if individuals with doomscrolling behavior continue to be exposed to more negative news in the long term, their doomscrolling behavior may be further exacerbated, thereby causing or worsening mental health issues. Therefore, we urge stakeholders to prioritize this important issue.

Revising the Chinese version of Doomscrolling Scale holds significant practical importance. Firstly, this measurement scale empowers researchers to assess individuals’ engagement in doomscrolling. Research has convincingly established a strong link between social media addiction and generalized problematic internet use, suggesting that doomscrolling represents an excessive and potentially dysfunctional facet of social media behavior [ 4 ]. Consequently, quantifying the doomscrolling behavior enables researchers to investigate it alongside other problematic behaviors. Secondly, increased awareness of doomscrolling behaviors can be beneficial for users, as it encourages the adoption of self-regulation strategies and alternative approaches to curbing excessive exposure to negative media. For example, users may learn to consume mobile news in smaller, deliberate portions [ 41 ]. Lastly, concerning the management of the current Chinese university students, utilizing this scale for assessment can inform decision-makers about the prevalence of negative information consumption and excessive social media use. Armed with this knowledge, it can assist mental health professionals in better identifying high-risk groups and implementing personalized interventions, as well as more precisely identifying college students who exhibit excessive reactions or are filled with fear and negative expectations towards the future due to negative information [ 6 , 42 ]. Thus, targeted and constructive measures can be implemented to curb these behaviors. For example, different intervention plans are provided for different degrees of doomscrolling, including psychological counseling and cognitive-behavioral therapy [ 43 ]. Addressing insomnia and depression arising from negative information through timely intervention can further prevent more serious psychological problems among college students.

Finally, we investigated the mediating role of doomscrolling between insomnia and depression. The results indicated that doomscrolling mediated the bidirectional relationship between insomnia and depression, with approximately half of the effect being mediated by doomscrolling. This further supports insomnia as one of the factors leading to depression, and the behavior of doomscrolling during insomnia may further exacerbate and perpetuate depressive symptoms [ 44 ]. Similarly, engaging in doomscrolling may also increase insomnia among individuals with depression [ 45 ]. These findings suggest that when university students experience insomnia, they should avoid doomscrolling, particularly at night.

To complement the lack of effective measurement tools for doomscrolling behavior in the Chinese scale, our revised Chinese version of the 15-item Doomscrolling Scale and the 4-item short version have good reliability and validity and can be used as measurement tools in studies of Chinese college students. In addition, the short version of Doomscrolling Scale is easy to use in conjunction with other addiction scales and mental disorder scales, and is also suitable for the time-series assessment of individual behaviors. The revision of this scale can promote the development of relevant research in China. On one hand, with the reports of doomscrolling behavior, Chinese college students gradually increase their awareness of doomscrolling, and may be wary of news media and social media full of negative content, thus reducing their unreasonable behavior of doomscrolling; On the other hand, the reports of relevant studies may attract the attention of university management and urge the implementation of effective management measures. Finally, we also call on relevant news or social media companies to block or reduce relevant negative information push. For example, the “personalized recommendation” function in the mobile app can be turned off to reduce browsing opportunities. In addition, the government should enact corresponding policies to supervise and restrict the information push mechanism of media companies.

Limitations

First, this study has certain limitations in terms of sample representativeness, mainly due to the uneven distribution of participant grades, small sample size, and the use of convenience sampling, which may result in a homogeneous sample and affect the reliability and generalizability of the results. Given that the participants in this study were primarily freshmen, the findings may not be representative of the overall university student population, especially considering that there may be differences across grades in dimensions such as depression, insomnia disorders, and mobile phone usage time. Similar influences may also arise from the age of the participants. Previous research has indicated that freshmen typically sleep less due to social media usage [ 46 ], and that the prevalence of depression is higher among seniors compared to freshmen [ 47 , 48 ]. These differences may affect university students’ doomscrolling behavior. Therefore, caution should be exercised when generalizing the findings of this study. Future studies should consider increasing the sample size and balancing the number of participants across different grades to enhance the representativeness and generalizability of the results.

Second, although we adopted an anonymous approach during the survey and used standardized scales, emphasizing that there were no right or wrong answers, the self-report method may still present response biases. For example, when answering the depression scale, individuals might respond in a socially desirable manner [ 49 ], often reporting fewer psychological issues. To address social desirability bias, future research could consider using diverse data collection methods for cross-validation or incorporating a social desirability scale to identify potentially biased responses [ 50 ]. Furthermore, some scales require participants to report their performance over a past period, which may be influenced by recall bias [ 51 ]. For instance, feelings of sadness experienced in the past two weeks might be forgotten at the time of response. Additionally, the Likert scale scoring method may lead to extreme response tendencies and fail to capture subtle changes. Future research could consider using scoring methods that can capture fine changes, such as the Visual Analog Scale [ 52 ].

Third, the use of a cross-sectional design in this study cannot capture the long-term effects of doomscrolling on mental health. This is particularly relevant as depressive symptoms and doomscrolling behavior are significantly influenced by time. Future research should consider adopting a longitudinal design to track individuals’ doomscrolling behavior and mental health over a period of time. This would help reveal the causal relationship between doomscrolling and mental health issues such as insomnia or depression, and provide valuable insights into the dynamic changes of related variables over time. Furthermore, a longitudinal design could serve as a basis for developing preventive interventions, especially in mitigating the negative impact of doomscrolling on mental health at an early stage.

Finally, in this study, we did not control confounding variables, which might affect the true relationship between the independent and dependent variables [ 53 ]. One potential confounding factor is the frequency of social media use. Studies have shown that excessive use of social media can lead to addiction [ 54 ], which is associated with severe depressive symptoms [ 55 ]. Similarly, prolonged social media use can delay sleep time, leading to insomnia [ 56 ]. Therefore, future research could consider how the social media use frequency or social media addiction influence the effects of doomscrolling on depression and insomnia. Another potential confounding factor is the type of negative news, which may include public health crises such as the COVID-19 pandemic in 2020 [ 57 ]; natural disasters such as the destruction caused by major earthquakes [ 58 ]; and crime and violence events. Exploring the role of news types in doomscrolling behavior will enhance our understanding of doomscrolling. In conclusion, controlling potential confounding variables is crucial for improving the internal validity of research when exploring the relationship between depression and insomnia disorders.

This study aimed to validate the Chinese version of Doomscrolling Scale among Chinese university students. The Chinese version demonstrated high reliability and validity, showing significant associations with depression, anxiety, and smartphone addiction. Additionally, it revealed that doomscrolling may mediate the relationship between insomnia disorder and depression. This validated measurement tool provides a foundation for further research on the mechanisms and impacts of doomscrolling on mental health.

Data availability

Data is provided upon request to the corresponding author.

Abbreviations

Quick response

Standard deviation

Patient health questionnaire

Diagnostic and statistical manual of mental disorders

Generalized anxiety disorder

Smartphone addiction scale short version

Exploratory factor analyses

Confirmatory factor analysis

Root mean square error of approximation

Comparative fit index

Tucker‒Lewis index

Incremental fit index

Standardized root mean square residual

Kaiser‒Meyer‒Olkin

Content validity index

Critical ratio

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T.W. and K.L. contributed to the conception and design of the study. L.Y., K.L., and X.T. wrote the main manuscript text, R.L. collected data and L.Y. prepared figures and tables. All authors reviewed the manuscript.

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Yang, L., Tan, X., Lang, R. et al. Reliability and validity of the Chinese version of the doomscrolling scale and the mediating role of doomscrolling in the bidirectional relationship between insomnia and depression. BMC Psychiatry 24 , 565 (2024). https://doi.org/10.1186/s12888-024-06006-5

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