effects of computer addiction to students research paper

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• Published: 10 December 2020

Effect of internet use and electronic game-play on academic performance of Australian children

• Md Irteja Islam 1 , 2 ,
• Raaj Kishore Biswas 3 &
• Rasheda Khanam 1  

Scientific Reports volume  10 , Article number:  21727 ( 2020 ) Cite this article

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This study examined the association of internet use, and electronic game-play with academic performance respectively on weekdays and weekends in Australian children. It also assessed whether addiction tendency to internet and game-play is associated with academic performance. Overall, 1704 children of 11–17-year-olds from young minds matter (YMM), a cross-sectional nationwide survey, were analysed. The generalized linear regression models adjusted for survey weights were applied to investigate the association between internet use, and electronic-gaming with academic performance (measured by NAPLAN–National standard score). About 70% of the sample spent > 2 h/day using the internet and nearly 30% played electronic-games for > 2 h/day. Internet users during weekdays (> 4 h/day) were less likely to get higher scores in reading and numeracy, and internet use on weekends (> 2–4 h/day) was positively associated with academic performance. In contrast, 16% of electronic gamers were more likely to get better reading scores on weekdays compared to those who did not. Addiction tendency to internet and electronic-gaming is found to be adversely associated with academic achievement. Further, results indicated the need for parental monitoring and/or self-regulation to limit the timing and duration of internet use/electronic-gaming to overcome the detrimental effects of internet use and electronic game-play on academic achievement.

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Introduction.

Over the past two decades, with the proliferation of high-tech devices (e.g. Smartphone, tablets and computers), both the internet and electronic games have become increasingly popular with people of all ages, but particularly with children and adolescents 1 , 2 , 3 . Recent estimates have shown that one in three under-18-year-olds across the world uses the Internet, and 75% of adolescents play electronic games daily in developed countries 4 , 5 , 6 . Studies in the United States reported that adolescents are occupied with over 11 h a day with modern electronic media such as computer/Internet and electronic games, which is more than they spend in school or with friends 7 , 8 . In Australia, it is reported that about 98% of children aged 15–17 years are among Internet users and 98% of adolescents play electronic games, which is significantly higher than the USA and Europe 9 , 10 , 11 , 12 .

In recent times, the Internet and electronic games have been regarded as important, not just for better results at school, but also for self-expression, sociability, creativity and entertainment for children and adolescents 13 , 14 . For instance, 88% of 12–17 year-olds in the USA considered the Internet as a useful mechanism for making progress in school 15 , and similarly, electronic gaming in children and adolescents may assist in developing skills such as decision-making, smart-thinking and coordination 3 , 15 .

On the other hand, evidence points to the fact that the use of the Internet and electronic games is found to have detrimental effects such as reduced sleeping time, behavioural problems (e.g. low self-esteem, anxiety, depression), attention problems and poor academic performance in adolescents 1 , 5 , 12 , 16 . In addition, excessive Internet usage and increased electronic gaming are found to be addictive and may cause serious functional impairment in the daily life of children and adolescents 1 , 12 , 13 , 16 . For example, the AU Kids Online survey 17 reported that 50% of Australian children were more likely to experience behavioural problems associated with Internet use compared to children from 25 European countries (29%) surveyed in the EU Kids Online study 18 , which is alarming 12 . These mixed results require an urgent need of understanding the effect of the Internet use and electronic gaming on the development of children and adolescents, particularly on their academic performance.

Despite many international studies and a smaller number in Australia 12 , several systematic limitations remain in the existing literature, particularly regarding the association of academic performance with the use of Internet and electronic games in children and adolescents 13 , 16 , 19 . First, the majority of the earlier studies have either relied on school grades or children’s self assessments—which contain an innate subjectivity by the assessor; and have not considered the standardized tests of academic performance 16 , 20 , 21 , 22 . Second, most previous studies have tested the hypothesis in the school-based settings instead of canvassing the whole community, and cannot therefore adjust for sociodemographic confounders 9 , 16 . Third, most studies have been typically limited to smaller sample sizes, which might have reduced the reliability of the results 9 , 16 , 23 .

By considering these issues, this study aimed to investigate the association of internet usage and electronic gaming on a standardized test of academic performance—NAPLAN (The National Assessment Program—Literacy and Numeracy) among Australian adolescents aged 11–17 years using nationally representative data from the Second Australian Child and Adolescent Survey of Mental Health and Wellbeing—Young Minds Matter (YMM). It is hypothesized that the findings of this study will provide a population-wide, contextual view of excessive Internet use and electronic games played separately on weekdays and weekends by Australian adolescents, which may be beneficial for evidence-based policies.

Subject demographics

Respondents who attended gave NAPLAN in 2008 (N = 4) and 2009 (N = 29) were removed from the sample due to smaller sample size, as later years (2010–2015) had over 100 samples yearly. The NAPLAN scores from 2008 might not align with a survey conducted in 2013. Further missing cases were deleted with the assumption that data were missing at random for unbiased estimates, which is common for large-scale surveys 24 . From the initial survey of 2967 samples, 1704 adolescents were sampled for this study.

The sample characteristics were displayed in Table 1 . For example, distribution of daily average internet use was checked, showing that over 50% of the sampled adolescents spent 2–4 h on internet (Table 1 ). Although all respondents in the survey used internet, nearly 21% of them did not play any electronic games in a day and almost one in every three (33%) adolescents played electronic games beyond the recommended time of 2 h per day. Girls had more addictive tendency to internet/game-play in compare to boys.

The mean scores for the three NAPLAN tests scores (reading, writing and numeracy) ranged from 520 to 600. A gradual decline in average NAPLAN tests scores (reading, writing and numeracy) scores were observed for internet use over 4 h during weekdays, and over 3 h during weekends (Table 2 ). Table 2 also shows that adolescents who played no electronic games at all have better scores in writing compared to those who play electronic games. Moreover, Table 2 shows no particular pattern between time spent on gaming and NAPLAN reading and numeracy scores. Among the survey samples, 308 adolescents were below the national standard average.

Internet use and academic performance

Our results show that internet (non-academic use) use during weekdays, especially more than 4 h, is negatively associated with academic performance (Table 3 ). For internet use during weekdays, all three models showed a significant negative association between time spent on internet and NAPLAN reading and numeracy scores. For example, in Model 1, adolescents who spent over 4 h on internet during weekdays are 15% and 17% less likely to get higher reading and numeracy scores respectively compared to those who spend less than 2 h. Similar results were found in Model 2 and 3 (Table 3 ), when we adjusted other confounders. The variable addiction tendency to internet was found to be negatively associated with NAPLAN results. The adolescents who had internet addiction were 17% less and 14% less likely to score higher in reading and numeracy respectively than those without such problematic behaviour.

Internet use during weekends showed a positive association with academic performance (Table 4 ). For example, Model 1 in Table 4 shows that internet use during weekends was significant for reading, writing and national standard scores. Youths who spend around 2–4 h and over 4 h on the internet during weekends were 21% and 15% more likely to get a higher reading scores respectively compared to those who spend less than 2 h (Model 1, Table 4 ). Similarly, in model 3, where the internet addiction of adolescents was adjusted, adolescents who spent 2–4 h on internet were 1.59 times more likely to score above the national standard. All three models of Table 4 confirmed that adolescents who spent 2–4 h on the internet during weekends are more likely to achieve better reading and writing scores and be at or above national standard compared to those who used the internet for less than 2 h. Numeracy scores were unlikely to be affected by internet use. The results obtained from Model 3 should be treated as robust, as this is the most comprehensive model that accounts for unobserved characteristics. The addiction tendency to internet/game-play variable showed a negative association with academic performance, but this is only significant for numeracy scores.

Electronic gaming and academic performance

Time spent on electronic gaming during weekdays had no effect on the academic performance of writing and language but had significant association with reading scores (Model 2, Table 5 ). Model 2 of Table 5 shows that adolescents who spent 1–2 h on gaming during weekdays were 13% more likely to get higher reading scores compared to those who did not play at all. It was an interesting result that while electronic gaming during weekdays tended to show a positive effect on reading scores, internet use during weekdays showed a negative effect. Addiction tendency to internet/game-play had a negative effect; the adolescents who were addicted to the internet were 14% less likely to score more highly in reading than those without any such behaviour.

All three models from Table 6 confirm that time spent on electronic gaming over 2 h during weekends had a positive effect on readings scores. For example, the results of Model 3 (Table 6 ) showed that adolescents who spent more than 2 h on electronic gaming during weekdays were 16% more likely to have better reading scores compared to adolescents who did not play games at all. Playing electronic games during weekends was not found to be statistically significant for writing and numeracy scores and national standard scores, although the odds ratios were positive. The results from all tables confirm that addiction tendency to internet/gaming is negatively associated with academic performance, although the variable is not always statistically significant.

Building on past research on the effect of the internet use and electronic gaming in adolescents, this study examined whether Internet use and playing electronic games were associated with academic performance (i.e. reading, writing and numeracy) using a standardized test of academic performance (i.e. NAPLAN) in a nationally representative dataset in Australia. The findings of this study question the conventional belief 9 , 25 that academic performance is negatively associated with internet use and electronic games, particularly when the internet is used for non-academic purpose.

In the current hi-tech world, many developed countries (e.g. the USA, Canada and Australia) have recommended that 5–17 year-olds limit electronic media (e.g. internet, electronic games) to 2 h per day for entertainment purposes, with concerns about the possible negative consequences of excessive use of electronic media 14 , 26 . However, previous research has often reported that children and adolescents spent more than the recommended time 26 . The present study also found similar results, that is, that about 70% of the sampled adolescents aged 11–17 spent more than 2 h per day on the Internet and nearly 30% spent more than 2-h on electronic gaming in a day. This could be attributed to the increased availability of computers/smart-phones and the internet among under-18s 12 . For instance, 97% of Australian households with children aged less than 15 years accessed internet at home in 2016–2017 10 ; as a result, policymakers recommended that parents restrict access to screens (e.g. Internet and electronic games) in children’s bedrooms, monitor children using screens, share screen hours with their children, and to act as role models by reducing their own screen time 14 .

This research has drawn attention to the fact that the average time spent using the internet, which is often more than 4 h during weekdays tends to be negatively associated with academic performance, especially a lower reading and numeracy score, while internet use of more than 2 h during weekends is positively associated with academic performance, particularly having a better reading and writing score and above national standard score. By dividing internet use and gaming by weekdays and weekends, this study find an answer to the mixed evidence found in previous literature 9 . The results of this study clearly show that the non-academic use of internet during weekdays, particularly, spending more than 4 h on internet is harmful for academic performance, whereas, internet use on the weekends is likely to incur a positive effect on academic performance. This result is consistent with a USA study that reported that internet use is positively associated with improved reading skills and higher scores on standardized tests 13 , 27 . It is also reported in the literature that academic performance is better among moderate users of the internet compared to non-users or high level users 13 , 27 , which was in line with the findings of this study. This may be due to the fact that the internet is predominantly a text-based format in which the internet users need to type and read to access most websites effectively 13 . The results of this study indicated that internet use is not harmful to academic performance if it is used moderately, especially, if ensuring very limited use on weekdays. The results of this study further confirmed that timing (weekdays or weekends) of internet use is a factor that needs to be considered.

Regarding electronic gaming, interestingly, the study found that the average time of gaming either in weekdays or weekends is positively associated with academic performance especially for reading scores. These results contradicted previous literatures 1 , 13 , 19 , 27 that have reported negative correlation between electronic games and educational performance in high-school children. The results of this study were consistent with studies conducted in the USA, Europe and other countries that claimed a positive correlation between gaming and academic performance, especially in numeracy and reading skills 28 , 29 . This is may be due to the fact that the instructions for playing most of the electronic games are text-heavy and many electronic games require gamers to solve puzzles 9 , 30 . The literature also found that playing electronic games develops cognitive skills (e.g. mental rotation abilities, dexterity), which can be attributable to better academic achievement 31 , 32 .

Consistent with previous research findings 33 , 34 , 35 , 36 , the study also found that adolescents who had addiction tendency to internet usage and/or electronic gaming were less likely to achieve higher scores in reading and numeracy compared to those who had not problematic behaviour. Addiction tendency to Internet/gaming among adolescents was found to be negatively associated with overall academic performance compared to those who were not having addiction tendency, although the variables were not always statistically significant. This is mainly because adolescents’ skipped school and missed classes and tuitions, and provide less effort to do homework due to addictive internet usage and electronic gaming 19 , 35 . The results of this study indicated that parental monitoring and/ or self-regulation (by the users) regarding the timing and intensity of internet use/gaming are essential to outweigh any negative effect of internet use and gaming on academic performance.

Although the present study uses a large nationally representative sample and advances prior research on the academic performance among adolescents who reported using the internet and playing electronic games, the findings of this study also have some limitations that need to be addressed. Firstly, adolescents who reported on the internet use and electronic games relied on self-reported child data without any screening tests or any external validation and thus, results may be overestimated or underestimated. Second, the study primarily addresses the internet use and electronic games as distinct behaviours, as the YMM survey gathered information only on the amount of time spent on internet use and electronic gaming, and included only a few questions related to addiction due to resources and time constraints and did not provide enough information to medically diagnose internet/gaming addiction. Finally, the cross-sectional research design of the data outlawed evaluation of causality and temporality of the observed association of internet use and electronic gaming with the academic performance in adolescents.

This study found that the average time spent on the internet on weekends and electronic gaming (both in weekdays and weekends) is positively associated with academic performance (measured by NAPLAN) of Australian adolescents. However, it confirmed a negative association between addiction tendency (internet use or electronic gaming) and academic performance; nonetheless, most of the adolescents used the internet and played electronic games more than the recommended 2-h limit per day. The study also revealed that further research is required on the development and implementation of interventions aimed at improving parental monitoring and fostering users’ self-regulation to restrict the daily usage of the internet and/or electronic games.

Data description

Young minds matter (YMM) was an Australian nationwide cross-sectional survey, on children aged 4–17 years conducted in 2013–2014 37 . Out of the initial 76,606 households approached, a total of 6,310 parents/caregivers (eligible household response rate 55%) of 4–17 year-old children completed a structured questionnaire via face to face interview and 2967 children aged 11–17 years (eligible children response rate 89%) completed a computer-based self-reported questionnaire privately at home 37 .

Area based sampling was used for the survey. A total of 225 Statistical Area 1 (defined by Australian Bureau of Statistics) areas were selected based on the 2011 Census of Population and Housing. They were stratified by state/territory and by metropolitan versus non-metropolitan (rural/regional) to ensure proportional representation of geographic areas across Australia 38 . However, a small number of samples were excluded, based on most remote areas, homeless children, institutional care and children living in households where interviews could not be conducted in English. The details of the survey and methodology used in the survey can be found in Lawrence et al. 37 .

Following informed consent (both written and verbal) from the primary carers (parents/caregivers), information on the National Assessment Program—Literacy and Numeracy (NAPLAN) of the children and adolescents were also added to the YMM dataset. The YMM survey is ethically approved by the Human Research Ethics Committee of the University of Western Australia and by the Australian Government Department of Health. In addition, the authors of this study obtained a written approval from Australian Data Archive (ADA) Dataverse to access the YMM dataset. All the researches were done in accordance with relevant ADA Dataverse guidelines and policy/regulations in using YMM datasets.

Outcome variables

The NAPLAN, conducted annually since 2008, is a nationwide standardized test of academic performance for all Australian students in Years 3, 5, 7 and 9 to assess their skills in reading, writing numeracy, grammar and spelling 39 , 40 . NAPLAN scores from 2010 to 2015, reported by YMM, were used as outcome variables in the models; while NAPLAN data of 2008 (N = 4) and 2009 (N = 29) were excluded for this study in order to reduce the time lag between YMM survey and the NAPLAN test. The NAPLAN gives point-in-time standardized scores, which provide the scope to compare children’s academic performance over time 40 , 41 . The NAPLAN tests are one component of the evaluation and grading phase of each school, and do not substitute for the comprehensive, consistent evaluations provided by teachers on the performance of each student 39 , 41 . All four domains—reading, writing, numeracy and language conventions (grammar and spelling) are in continuous scales in the dataset. The scores are given based on a series of tests; details can be found in 42 . The current study uses only reading, writing and numeracy scores to measure academic performance.

In this study, the National standard score is a combination of three variables: whether the student meets the national standard in reading, writing and numeracy. Based on national average score, a binary outcome variable is also generated. One category is ‘below standard’ if a child scores at least one standard deviation (one below scores) from the national standard in reading, writing and numeracy, and the rest is ‘at/above standard’.

Independent variables

Internet use and electronic gaming.

In the YMM survey, owing to the scope of the survey itself, an extensive set of questions about internet usage and electronic gaming could not be included. Internet usage omitted the time spent in academic purposes and/or related activities. Playing electronic games included playing games on a gaming console (e.g. PlayStation, Xbox, or similar console ) online or using a computer, or mobile phone, or a handled device 12 . The primary independent covariates were average internet use per day and average electronic game-play in hours per day. A combination of hours on weekdays and weekends was separately used in the models. These variables were based on a self-assessed questionnaire where the youths were asked questions regarding daily time spent on the Internet and electronic game-play, specifically on either weekends or weekdays. Since, internet use/game-play for a maximum of 2 h/day is recommended for children and adolescents aged between 5 and 17 years in many developed countries including Australia 14 , 26 ; therefore, to be consistent with the recommended time we preferred to categorize both the time variables of internet use and gaming into three groups with an interval of 2 h each. Internet use was categorized into three groups: (a) ≤ 2 h), (b) 2–4 h, and (c) > 4 h. Similar questions were asked for game-play h. The sample distribution for electronic game-play was skewed; therefore, this variable was categorized into three groups: (a) no game-play (0 h), (b) 1–2 h, and (c) > 2 h.

Other covariates

Family structure and several sociodemographic variables were used in the models to adjust for the differences in individual characteristics, parental inputs and tastes, household characteristics and place of residence. Individual characteristics included age (continuous) and sex of the child (boys, girls) and addiction tendency to internet use and/or game-play of the adolescent. Addiction tendency to internet/game-play was a binary independent variable. It was a combination of five behavioural questions relating to: whether the respondent avoided eating/sleeping due to internet use or game-play; feels bothered when s/he cannot access internet or play electronic games; keeps using internet or playing electronic games even when s/he is not really interested; spends less time with family/friends or on school works due to internet use or game-play; and unsuccessfully tries to spend less time on the internet or playing electronic games. There were four options for each question: never/almost never; not very often; fairly often; and very often. A binary covariate was simulated, where if any four out of five behaviours were reported as for example, fairly often or very often, then it was considered that the respondent had addictive tendency.

Household characteristics included household income (low, medium, high), family type (original, step, blended, sole parent/primary carer, other) 43 and remoteness (major cities, inner regional, outer regional, remote/very remote). Parental inputs and taste included education of primary carer (bachelor, diploma, year 10/11), primary carer’s likelihood of serious mental illness (K6 score -likely; not likely); primary carer’s smoking status (no, yes); and risk of alcoholic related harm by the primary carer (risky, none).

Statistical analysis

Descriptive statistics of the sample and distributions of the outcome variables were initially assessed. Based on these distributions, the categorization of outcome variables was conducted, as mentioned above. For formal analysis, generalized linear regression models (GLMs) 44 were used, adjusting for the survey weights, which allowed for generalization of the findings. As NAPLAN scores of three areas—reading, writing and numeracy—were continuous variables, linear models were fitted to daily average internet time and electronic game play time. The scores were standardized (mean = 0, SD = 1) for model fitness. The binary logistic model was fitted for the dichotomized national standard outcome variable. Separate models were estimated for internet and electronic gaming on weekends and weekdays.

We estimated three different models, where models varied based on covariates used to adjust the GLMs. Model 1 was adjusted for common sociodemographic factors including age and sex of the child, household income, education of primary carer’s and family type 43 . However, the results of this model did not account for some unobserved household characteristics (e.g. taste, preferences) that are unobserved to the researcher and are arguably correlated with potential outcomes. The effects of unobserved characteristics were reduced by using a comprehensive set of observable characteristics 45 , 46 that were available in YMM data. The issue of unobserved characteristics was addressed by estimating two additional models that include variables by including household characteristics such as parental taste, preference and inputs, and child characteristics in the model. In addition to the variables in Model 1, Model 2 included remoteness, primary carer’s mental health status, smoking status and risk of alcoholic related harm by the primary carer. Model 3 further included internet/game addiction of the adolescent in addition to all the covariates in Model 2. Model 3 was expected to account for a child’s level of unobserved characteristics as the children who were addicted to internet/games were different from others. The model will further show how academic performance is affected by internet/game addiction. The correlation among the variables ‘internet/game addiction’ and ‘internet use’ and ‘gaming’ (during weekdays and weekends) were also assessed, and they were less than 0.5. Multicollinearity was assessed using the variance inflation factor (VIF), which was under 5 for all models, suggesting no multicollinearity 47 .

p value below the threshold of 0.05 was considered the threshold of significance. All analysis was conducted in R (version 3.6.1). R-package survey (version 3.37) was used for modelling which is suited for complex survey samples 48 .

Data availability

The authors declare that they do not have permission to share dataset. However, the datasets of Young Minds Matter (YMM) survey data is available at the Australian Data Archive (ADA) Dataverse on request ( https://doi.org/10.4225/87/LCVEU3 ).

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Acknowledgements

The authors would like to thank the University of Western Australia, Roy Morgan Research, the Australian Government Department of Health for conducting the survey, and the Australian Data Archive for giving access to the YMM survey dataset. The authors also would like to thank Dr Barbara Harmes for proofreading the manuscript.

This research did not receive any specific Grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Md Irteja Islam & Rasheda Khanam

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Transport and Road Safety (TARS) Research Centre, School of Aviation, University of New South Wales, Sydney, NSW, 2052, Australia

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Islam, M.I., Biswas, R.K. & Khanam, R. Effect of internet use and electronic game-play on academic performance of Australian children. Sci Rep 10 , 21727 (2020). https://doi.org/10.1038/s41598-020-78916-9

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Current Research and Viewpoints on Internet Addiction in Adolescents

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

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

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Purpose of Review

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

Recent Findings

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

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

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Introduction

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

Defining the Issue

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

A Focus on Gaming

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

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

Scales and Assessment

Building effective igd scales.

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

Internet Addiction Scales

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

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

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

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

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

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

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

Potential Determinants of PIMU

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

Family Functioning

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

Personality Traits

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

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

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

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

Neurobiology and Brain Function

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

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

Comorbidities

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

Depression and Anxiety

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

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

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

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

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

Cognitive Behavioral Therapy

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

Pharmacological Treatment

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

Group and Family Therapies

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

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

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

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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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ORIGINAL RESEARCH article

The effects of online game addiction on reduced academic achievement motivation among chinese college students: the mediating role of learning engagement.

• 1 BinZhou College of Science and Technology, Binzhou, China
• 2 Binzhou Polytechnic, Binzhou, China
• 3 Faculty of Education, Beijing Normal University, Beijing, China
• 4 National Institute of Vocational Education, Beijing Normal University, Beijing, China

Introduction: The present study aimed to examine the effects of online game addiction on reduced academic achievement motivation, and the mediating role of learning engagement among Chinese college students to investigate the relationships between the three variables.

Methods: The study used convenience sampling to recruit Chinese university students to participate voluntarily. A total of 443 valid questionnaires were collected through the Questionnaire Star application. The average age of the participants was 18.77 years old, with 157 males and 286 females. Statistical analysis was conducted using SPSS and AMOS.

Results: (1) Chinese college students’ online game addiction negatively affected their behavioral, emotional, and cognitive engagement (the three dimensions of learning engagement); (2) behavioral, emotional, and cognitive engagement negatively affected their reduced academic achievement motivation; (3) learning engagement mediated the relationship between online game addiction and reduced academic achievement motivation.

1. Introduction

Online games, along with improvements in technology, have entered the daily life of college students through the popularity of computers, smartphones, PSPs (PlayStation Portable), and other gaming devices. Online game addiction has recently become a critical problem affecting college students’ studies and lives. As early as 2018, online game addiction was officially included in the category of “addictive mental disorders” by the World Health Organization (WHO), and the International Classification of Diseases (ICD) was updated specifically to include the category of “Internet Gaming Disorder” (IGD). Prior research investigating Chinese college students’ online game addiction status mostly comprised regional small-scale studies. For example, a study on 394 college students in Chengde City, Hebei province, China showed that the rate of online game addiction was about 9% ( Cui et al., 2021 ). According to the results of an online game survey conducted by China Youth Network (2019) on 682 Chinese college students who played online games, nearly 60% of participants played games for more than 1 h a day, over 30% stayed up late because of playing games, over 40% thought that playing games had affected their physical health, over 70% claimed that games did not affect their studies, and over 60% had spent money on online games. This phenomenon has been exacerbated by the fact that smartphones and various portable gaming devices have become new vehicles for gaming with the development of technology. The increase in the frequency or time spent on daily gaming among adolescents implies a growth in the probability of gaming addiction, while an increase in the level of education decreases the probability of gaming addiction ( Esposito et al., 2020 ; Kesici, 2020 ). Moreover, during the COVID-19 pandemic, adolescents’ video game use and the severity of online gaming disorders increased significantly ( Teng et al., 2021 ).

A large body of literature on the relationship between problematic smartphone use and academic performance has illustrated the varying adverse effects of excessive smartphone obsession ( Durak, 2018 ; Mendoza et al., 2018 ; Rozgonjuk et al., 2018 ). These effects are manifested in three critical ways: first, the more frequently cell phones are used during study, the greater the negative impact on academic performance and achievement; second, students are required to master the basic skills and cognitive abilities to succeed academically, which are negatively affected by excessive cell phone use and addiction ( Sunday et al., 2021 ); third, online game addiction negatively affects students’ learning motivation ( Demir and Kutlu, 2018 ; Eliyani and Sari, 2021 ). However, there is currently a lack of scientifically objective means of effective data collection regarding online game addiction among college students in China, such as big data. Hong R. Z. et al. (2021) and Nong et al. (2023) suggested that the impact of addiction on students’ learning should be explored more deeply.

Since the 1990s, learning engagement has been regarded as a positive behavioral practice in learning in Europe and the United States, and plays an important role in the field of higher education research ( Axelson and Flick, 2010 ). Recently, studies on learning engagement among college students have also been a hot topic in various countries ( Guo et al., 2021 ). According to Fredricks et al. (2004) , learning engagement includes three dimensions: behavioral, emotional, and cognitive.

The concept of behavioral engagement encompasses three aspects: first, positive behavior in the classroom, such as following school rules and regulations and classroom norms; second, engagement in learning; and third, active participation in school activities ( Finn et al., 1995 ). Emotional engagement refers to students’ responses to their academic content and learning environment. The emotional responses to academic content include students’ emotional responses such as interest or disinterest in learning during academic activities ( Kahu and Nelson, 2018 ), while the emotional responses to the learning environment refer to students’ identification with their peers, teachers, and the school environment ( Stipek, 2002 ). Cognitive engagement is often associated with internal processes such as deep processing, using cognitive strategies, self-regulation, investment in learning, the ability to think reflectively, and making connections in daily life ( Khan et al., 2017 ). Cognitive engagement emphasizes the student’s investment in learning and self-regulation or strategies.

According to Yang X. et al. (2021) , learning engagement refers to students’ socialization, behavioral intensity, affective qualities, and use of cognitive strategies in performing learning activities. Besides, Kuh et al. (2007) argued that learning engagement was “the amount of time and effort students devote to instructional goals and meaningful educational practices.” Learning engagement is not only an important indicator of students’ learning process, but also a significant predictor of students’ academic achievement ( Zhang, 2012 ). It is also an essential factor in promoting college students’ academic success and improving education quality.

As one of the crucial components of students’ learning motivation ( Han and Lu, 2018 ), achievement motivation is the driving force behind an individual’s efforts to put energy into what he or she perceives to be valuable and meaningful to achieve a desired outcome ( Story et al., 2009 ). It can be considered as achievement motivation when an individual’s behavior involves “competing at a standard of excellence” ( Brunstein and Heckhausen, 2018 ). Students’ achievement motivation ensures the continuity of learning activities, achieving academic excellence and desired goals ( Sopiah, 2021 ). Based on the concept of achievement motivation, academic achievement motivation refers to the mental perceptions or intentions that students carry out regarding their academic achievement, a cognitive structure by which students perceive success or failure and determine their behavior ( Elliot and Church, 1997 ). Related research also suggests that motivation is one variable that significantly predicts learning engagement ( Xiong et al., 2015 ).

Therefore, it is worthwhile to investigate the internal influence mechanism of college students’ online game addiction on their reduced academic achievement motivation and the role of learning engagement, which is also an issue that cannot be ignored in higher education research. The present study explored the relationship between online game addiction, learning engagement, and reduced academic achievement motivation among college students by establishing a structural equation model (SEM) to shed light on the problem of online game addiction among college students.

2. Research model and hypotheses

2.1. research model.

Previous research usually regarded learning engagement as a variable of one or two dimensions, and scholars tend to favor the dimension of behavioral engagement. However, other ignored dimensions are inseparable parts of learning engagement ( Dincer et al., 2019 ). In a multi-dimensional model, the mutual terms of each dimension form a single composite structure. Therefore, the present study took the structure proposed by Fredricks et al. (2004) as a reference, divided learning engagement into behavioral, emotional, and cognitive dimensions as mediating variables, and explored the relationship between online game addiction, learning engagement, and reduced academic achievement motivation. The research frame diagram is shown in Figure 1 .

Figure 1 . The research model.

2.2. Research questions

2.2.1. the relationship between online game addiction and learning engagement.

Learning engagement has been viewed as a multidimensional concept in previous studies. Finn (1989) proposed the participation-identification model to make pioneering progress in learning engagement study. Schaufeli et al. (2002) suggested that learning engagement was an active, fulfilling mental state associated with learning. Chapman (2002) pointed out affective, behavioral, and cognitive criteria for assessing students’ learning engagement based on previous research. Fredricks et al. (2004) systematically outlined learning engagement as an integration of behavioral, emotional, and cognitive engagement. The updated International Classification of Diseases [ World Health Organization (WHO), 2018a , b ] specifies several diagnostic criteria for gaming addiction, including the abandonment of other activities, the loss of interest in other previous hobbies, and the loss or potential loss of work and social interaction because of gaming. Past studies have shown the adverse effects of excessive Internet usage on students’ learning. Short video addiction negatively affects intrinsic and extrinsic learning motivation ( Ye et al., 2022 ). Students’ cell phone addiction negatively affects academic commitment, academic performance, and relationship facilitation, all of which negatively affect their academic achievement ( Tian et al., 2021 ). The amount of time spent surfing the Internet and playing games has been identified to negatively affect students’ cognitive ability ( Pan et al., 2022 ). College students’ cell phone addiction, mainly reflected in cell phone social addiction and game entertainment addiction, has also been noted to impact learning engagement; specifically, the higher the level of addiction, the lower the learning engagement ( Qi et al., 2020 ). Gao et al. (2021) also showed that cell phone addiction among college students could negatively affect their learning engagement. Choi (2019) showed that excessive use of cell phones might contribute to smartphone addiction, which also affects students’ learning engagement. Accordingly, the following three research hypotheses were proposed.

H1 : Online game addiction negatively affects behavioral engagement.

H2 : Online game addiction negatively affects emotional engagement.

H3 : Online game addiction negatively affects cognitive engagement.

2.2.2. The relationship between learning engagement and reduced academic achievement motivation

Achievement motivation is people’s pursuit of maximizing individual value, which embodies an innate drive, including the need for achievement, and can be divided into two parts: the intention to succeed and the intention to avoid failure ( McClelland et al., 1976 ). On this basis, Weiner (1985) proposed the attributional theory of achievement motivation, suggesting that individuals’ personality differences, as well as the experience of success and failure, could influence their achievement attributions and that an individual’s previous achievement attributions would affect his or her expectations and emotions for the subsequent achievement behavior while expectations and emotions could guide motivated behavior. Birch and Ladd (1997) indicated that behavioral engagement involved positive behavioral attitudes such as hard work, persistence, concentration, willingness to ask questions, and active participation in class discussions to complete class assignments. Students’ attitudes toward learning are positively related to achievement motivation ( Bakar et al., 2010 ). Emotional engagement involves students’ sense of identity with their peers, teachers, and the school environment ( Stipek, 2002 ). Students’ perceptions of the school environment influence their achievement motivation ( Wang and Eccles, 2013 ). Cognitive engagement encompasses the ability to use cognitive strategies, self-regulation, investment in learning, and reflective thinking ( Khan et al., 2017 ). Learning independence and problem-solving abilities predict student motivation ( Saeid and Eslaminejad, 2017 ). Hu et al. (2021) indicated that cognitive engagement had the most significant effect on students’ academic achievement among the learning engagement dimensions, and that emotional engagement was also an important factor influencing students’ academic achievement. Therefore, the following three research hypotheses were proposed:

H4 : Behavioral engagement significantly and negatively affects the reduced academic achievement motivation.

H5 : Emotional engagement significantly and negatively affects the reduced academic achievement motivation.

H6 : Cognitive engagement significantly and negatively affects the reduced academic achievement motivation.

2.2.3. The relationship between online game addiction, learning engagement, and reduced academic achievement motivation

Past studies have demonstrated the relationship between online game addiction and students’ achievement motivation. For example, a significant negative correlation between social network addiction and students’ motivation to progress has been reported ( Haji Anzehai, 2020 ), and a significant negative correlation between Internet addiction and students’ achievement motivation has been reported ( Cao et al., 2008 ). Students addicted to online games generally have lower academic achievement motivation because they lack precise academic planning and motivation ( Chen and Gu, 2019 ). Yayman and Bilgin (2020) pointed out a correlation between social media addiction and online game addiction. Accordingly, there might be a negative correlation between online game addiction and academic achievement motivation among college students.

Students addicted to online games generally have lower motivation for academic achievement because they lack precise academic planning and learning motivation ( Chen and Gu, 2019 ). Similarly, Haji Anzehai (2020) reported a significant negative correlation between social network addiction and students’ motivation to progress.

Learning engagement is often explored as a mediating variable in education research. Zhang et al. (2018) found that learning engagement was an essential mediator of the negative effect of internet addiction on academic achievement in late adolescence and is a key factor in the decline in academic achievement due to students’ internet addiction. Li et al. (2019) noted that college students’ social networking site addiction significantly negatively affected their learning engagement, and learning engagement mediated the relationship between social networking addiction and academic achievement. Accordingly, the following research hypothesis was proposed.

H7 : Learning engagement mediates the relationship between online game addiction and reduced academic achievement motivation.

3. Research methodology and design

3.1. survey implementation.

The present study employed the Questionnaire Star application for online questionnaire distribution. Convenience sampling was adopted to recruit Chinese college students to participate voluntarily. The data were collected from October 2021 to January 2022 from a higher vocational college in Shandong province, China. Participants were first-and second-year students. According to Shumacker and Lomax (2016) , the number of participants in SEM studies should be approximately between 100 and 500 or more. In the present study, 500 questionnaires were returned, and 443 were valid after excluding invalid responses. The mean age of the participants was 18.77 years. There were 157 male students, accounting for 35.4% of the total sample, and 286 female students, accounting for 64.6%.

3.2. Measurement instruments

The present empirical study employed quantitative research methods by collecting questionnaires for data analysis. The items of questionnaires were adapted from research findings based on corresponding theories and were reviewed by experts to confirm the content validity of the instruments. The distributed questionnaire was a Likert 5-point scale (1 for strongly disagree , 2 for disagree , 3 for average , 4 for agree , and 5 for strongly agree ). After the questionnaire was collected, item analysis was conducted first, followed by reliability and validity analysis of the questionnaire constructs using SPSS23 to test whether the scale met the criteria. Finally, research model validation was conducted.

3.2.1. Online game addiction

In the present study, online game addiction referred to the addictive behavior of college students in online games, including mobile games and online games. The present study adopted a game addiction scale compiled by Wu et al. (2021) and adapted the items based on the definition of online game addiction. The adapted scale had 10 items. Two examples of the adapted items in the scale were: “I will put down what should be done and spend my time playing online games” and “My excitement or expectation of playing an online game is far better than other interpersonal interactions.”

3.2.2. Learning engagement

In the present study, learning engagement included students’ academic engagement in three dimensions: behavioral, emotional, and cognitive. The learning engagement scale compiled by Luan et al. (2020) was adapted based on its definition. The adapted scale had 26 questions in three dimensions: behavioral, emotional, and cognitive engagement. Two examples of the adapted items in the scale are: “I like to actively explore unfamiliar things when I am doing my homework” and “I will remind myself to double-check the places where I tend to make mistakes in my homework.”

3.2.3. Reduced academic achievement motivation

Reduced academic achievement motivation in the present study refers to the reduction in college students’ intrinsic tendency to enjoy challenges and achieve academic goals and academic success. The achievement motivation scale developed by Ye et al. (2020) was adapted to measure reduced academic achievement motivation. The adapted scale had 10 items. Two examples of the adapted items in the scale are: “Since playing online games, I do not believe that the effectiveness of learning is up to me, but that it depends on other people or the environment” and “Since I often play online games, I am satisfied with my current academic performance or achievement and do not seek higher academic challenges.”

4. Results and discussion

4.1. internal validity analysis of the measurement instruments.

In the present study, item analysis was conducted using first-order confirmatory factor analysis (CFA), which can reflect the degree of measured variables’ performance within a smaller construct ( Hafiz and Shaari, 2013 ). The first-order CFA is based on the streamlined model and the principle of independence of residuals. According to Hair et al. (2010) and Kenny et al. (2015) , it is recommended that the value of χ 2 / df in the model fitness indices should be less than 5; the root mean square error of approximation (RMSEA) value should be greater than 0.100; the values of the goodness of fit index (GFI) and adjusted goodness of fit index (AGFI) should not be lower than 0.800; the factor loading (FL) values of the constructs should also be greater than 0.500. Based on the criteria above, the items measuring the online game addiction construct were reduced from 10 to seven; the items measuring the behavioral engagement construct were reduced from nine to six; the items measuring the emotional engagement construct were reduced from nine to six; the items measuring the cognitive engagement construct were reduced from eight to six; and the items measuring the reduced academic achievement motivation construct was reduced from 10 to six, as shown in Table 1 .

Table 1 . First-order confirmatory factor analysis.

4.2. Construct reliability and validity analysis

In order to determine the internal consistency of the constructs, the reliability of the questionnaire was tested using Cronbach’ s α value. According to Hair et al. (2010) , a Cronbach’ s α value greater than 0.700 indicates an excellent internal consistency among the items, and the constructs’ composite reliability (CR) values should exceed 0.700 to meet the criteria. In the present study, the Cronbach’ s α values for the constructs ranged from 0.911 to 0.960, and the CR values ranged from 0.913 to 0.916, which met the criteria, as shown in Table 2 .

Table 2 . Construct reliability and validity of constructs.

In the present study, convergent validity was confirmed by two types of indicators, FL and average variance extracted (AVE). According to Hair et al. (2011) , an FL value should be greater than 0.500, and items with an FL value less than 0.500 should be removed; and AVE values should be greater than 0.500. In the present study, the FL values of the constructs ranged from 0.526 to 0.932, and the AVE values ranged from 0.600 to 0.805; all dimensions met the recommended criteria, as shown in Table 2 .

According to Awang (2015) and Hair et al. (2011) , the square root of the AVE of each construct (latent variable) should be greater than its correlation coefficient values with other constructs to indicate the ideal discriminant validity. The results of the present study showed that the three constructs of online game addiction, learning engagement, and reduced academic achievement motivation had good discriminant validity in the present study, as shown in Table 3 .

Table 3 . Discriminant validity analysis.

4.3. Correlation analysis

Pearson’s correlation coefficient is usually used to determine the closeness of the relationship between variables. A correlation coefficient greater than 0.8 indicates a high correlation between variables; a correlation coefficient between 0.3 and 0.8 indicates a moderate correlation between variables; while a correlation of less than 0.3 indicates a low correlation. Table 4 shows the Correlation Analysis results. Online game addiction was moderately negatively correlated with behavioral engagement ( r  = −0.402, p  < 0.001), moderately negatively correlated with emotional engagement ( r  = −0.352, p  < 0.001), slightly negatively correlated with cognitive engagement ( r  = −0.288, p  < 0.001), and slightly positively correlated with reduced academic achievement motivation ( r  = 0.295, p  < 0.001). Behavioral engagement was moderately positively correlated with emotional engagement ( r  = 0.696, p  < 0.001), moderately positively correlated with cognitive engagement ( r  = 0.601, p  < 0.001), and moderately negatively correlated with reduced academic achievement motivation ( r  = −0.497, p  < 0.001). Emotional engagement was moderately positively correlated with cognitive engagement ( r  = 0.787, p  < 0.001) and moderately negatively correlated with reduced academic achievement motivation ( r  = −0.528, p  < 0.001). Cognitive engagement was moderately negatively correlated with reduced motivation for academic achievement ( r  = −0.528, p  < 0.001).

Table 4 . Correlation analysis.

4.4. Analysis of fitness of the measurement model

According to Hair et al. (2010) and Abedi et al. (2015) , the following criteria should be met in the analysis for measurement model fitness: the ratio of chi-squared and degree of freedom ( χ 2 / df ) should be less than 5; the root mean square error of approximation (RMSEA) should not exceed 0.100; the goodness of fit index (GFI), adjusted goodness of fit index (AGFI), normed fit index (NFI), non-normed fit index (NNFI), comparative fit index (CFI), incremental fit index (IFI) and relative fit index (RFI) should be higher than 0.800; and the parsimonious normed fit index (PNFI) and the parsimonious fitness of fit index (PGFI) should be higher than 0.500. The model fitness indices in the present study were χ 2  = 1434.8, df  = 428, χ 2 / df  = 3.352, RMSEA = 0.073, GFI = 0.837, AGFI = 0.811, NFI = 0.899, NNFI = 0.920, CFI = 0.927, IFI = 0.927, RFI = 0.890, PNFI = 0.827, and PGFI = 0.722. The results were in accordance with the criteria, indicating a good fitness of the model in the present study ( Table 5 ).

Table 5 . Direct effects analysis.

4.5. Validation of the research model

Online game addiction had a negative effect on behavioral engagement ( β  = −0.486; t  = −9.143; p < 0.001). Online game addiction had a negative effect on emotional engagement ( β  = −0.430; t  = −8.054; p < 0.001). Online game addiction had a negative effect on cognitive engagement ( β  = −0.370; t  = −7.180; p < 0.001). Online game addiction had a positive effect on reduced academic achievement motivation ( β  = 0.19; t = −2.776; p < 0.01). Behavioral engagement had a negative effect on reduced academic achievement motivation ( β  = −0.238; t  = −3.759; p < 0.001). Emotional engagement had a negative effect on reduced academic achievement motivation ( β  = −0.221; t  = −2.687; p < 0.01), and cognitive engagement had a negative effect on reduced academic achievement motivation ( β  = −0.265; t  = −3.581; p < 0.01), as shown in Figure 2 Table 6 .

Figure 2 . Validation of the research model. *** p  < 0.001.

Table 6 . Indirect effects analysis.

Cohen’ s f 2 is an uncommon but valuable standardized effect size measure that can be used to assess the size of local effects ( Selya et al., 2012 ). When f 2 reaches 0.02 it represents a small effect size, 0.150 represents a medium effect size, and 0.350 represents a high effect size ( Hair et al., 2014 ). The explanatory power of online game addiction on behavioral engagement was 23.6%, and f 2 was 0.309. The explanatory power of online game addiction on emotional engagement was 18.5%, and f 2 was 0.227. The explanatory power of online game addiction on cognitive engagement was 13.7%, and f 2 was 0.159. The explanatory power of behavioral, emotional, and cognitive engagement on reduced academic achievement motivation was 23.9%, and f 2 was 0.314. Figure 2 illustrates the above findings.

4.6. Indirect effects analysis

Scholars are often interested in whether variables mediate the association between predicting and outcome variables. Therefore, mediating variables can partially or entirely explain the association ( Hwang et al., 2019 ). In research fields such as psychology and behavior, where the research situation is often more complex, multiple mediating variables are often required to clearly explain the effects of the independent variables on the dependent variables ( MacKinnon, 2012 ). Scientific quantitative research requires tests of confidence interval (CI; Thompson, 2002 ), and the standard value of the test numbers is often determined by 95% CI ( Altman and Bland, 2011 ). CI value not containing 0 indicates the statistical significance of the analysis results ( Nakagawa and Cuthill, 2007 ). According to the statistical results shown in Table 4 , behavioral engagement significantly positively mediated the relationship between online game addiction and reduced academic achievement motivation with a path coefficient of 0.230 and 95% CI ranging from 0.150 to 0.300 (excluding 0), p < 0.01; emotional engagement positively mediated the relationship between online game addiction and reduced academic achievement motivation with a path coefficient of 0.209, 95% CI ranging from 0.130 to 0.292 (excluding 0), p < 0.01; cognitive engagement positively mediated the relationship between online game addiction and reduced academic achievement motivation with a path coefficient of 0.170, 95% CI ranging from 0.100 to 0.250 (excluding 0), p < 0.01, as shown in Table 6 .

4.7. Discussion

4.7.1. analysis of the relationship between online game addiction and learning engagement.

Online game addiction is often negatively associated with students’ learning. For example, the problematic use of short videos was reported as negatively affecting students’ behavioral engagement, while behavioral engagement positively affected students’ emotional and cognitive engagement ( Ye et al., 2023 ). Meral (2019) highlighted that students’ learning attitudes and academic performance had a negative relationship with students’ addiction to online games. Demir and Kutlu (2018) found that online game addiction negatively affects students’ learning motivation. As the level of students’ game addiction increased, the level of their communication skills decreased ( Kanat, 2019 ). Furthermore, Tsai et al. (2020) pointed out a negative correlation between online game addiction and peer relationships as well as students’ learning attitudes. According to the results of the research model validation, it can be observed that: online game addiction negatively affected behavioral engagement, emotional engagement, and cognitive engagement. Therefore, it can be stated that online game addiction had significant and negative effects on all dimensions of learning engagement.

Online game addiction in the present study included aspects of computer game addiction and mobile phone game addiction. The results of the present study are consistent with the findings of Gao et al. (2021) , Choi (2019) , and Qi et al. (2020) , who pointed out that college students’ addiction to cell phones negatively affected their learning engagement.

4.7.2. Analysis of the relationship between learning engagement and reduced academic achievement motivation

For technology education in higher education, students’ intrinsic motivation for academic study predicts their learning engagement ( Dunn and Kennedy, 2019 ). In addition, learning engagement is positively correlated with academic achievement ( Fredricks and McColskey, 2012 ). Based on the research model validation results, behavioral, emotional, and cognitive engagement all negatively affected reduced academic achievement motivation. The findings are consistent with Hu et al.’s (2021) study which pointed out that cognitive engagement in the learning engagement dimension had the most significant effect on students’ academic achievement, and that emotional engagement was also an essential factor influencing students’ academic achievement. Lau et al. (2008) showed that achievement motivation positively predicted cognitive engagement in the learning engagement dimension. Mih et al. (2015) noted that achievement motivation positively predicted behavioral and emotional engagement in the learning engagement dimension. The present study supported the above discussion by confirming the association between learning engagement and reduced academic achievement motivation.

4.7.3. Analysis of the mediating role of learning engagement

According to the indirect effects analysis results of the present study, learning engagement negatively mediated the relationship between online game addiction and reduced academic achievement motivation. The findings support Haji Anzehai’s (2020) conclusion that social network addiction negatively correlated with students’ motivation to progress ( Haji Anzehai, 2020 ). It is also consistent with the findings of Chen and Gu (2019) that students addicted to online games generally had lower academic achievement motivation due to a lack of precise academic planning and motivation. Cao et al. (2008) found a significant negative correlation between Internet addiction and students’ achievement motivation. Similarly, Zhang et al. (2018) explored the intrinsic influencing mechanism of students’ Internet addiction on academic achievement decline in their late adolescence by identifying learning engagement as the important mediating variable. Li et al. (2019) proposed that social networking site addiction among college students significantly negatively affected learning engagement and that learning engagement mediated the relationship between social network addiction and students’ academic achievement. The present study findings also support the discussion above.

5. Conclusion and suggestions

5.1. conclusion.

Currently, the problem of online game addiction among college students is increasing. The relationship between online game addiction, learning engagement, and reduced academic achievement motivation still needs to be explored. The present study explored the relationships between the three aforementioned variables by performing SEM. The results of the study indicated that: (1) online game addiction negatively affected behavioral engagement; (2) online game addiction negatively affected emotional engagement; (3) online game addiction negatively affected cognitive engagement; (4) behavioral engagement negatively affected reduced academic achievement motivation; (5) emotional engagement negatively affected reduced academic achievement motivation; (6) cognitive behavioral engagement negatively affected reduced academic achievement motivation; (7) learning engagement mediated the relationship between online game addiction and reduced academic achievement motivation.

According to the research results, when college students are addicted to online games, their learning engagement can be affected, which may decrease their behavioral, emotional, and cognitive engagement; their academic achievement motivation may be further reduced and affect their academic success or even prevent them from completing their studies. The mediating role of learning engagement between online game addiction and reduced academic achievement motivation indicates that reduced academic achievement motivation influenced by online game addiction could be prevented or weakened by enhancing learning engagement.

5.2. Suggestions

Universities and families play a crucial role in preventing online game addiction among college students. One of the main reasons college students play online games may be that they lack an understanding of other leisure methods and can only relieve their psychological pressure through online games ( Fan and Gai, 2022 ). Therefore, universities should enrich college students’ after-school leisure life and help them cultivate healthy hobbies and interests. Besides, a harmonious parent–child relationship positively affects children’s learning engagement ( Shao and Kang, 2022 ). Parents’ stricter demands may aggravate children’s game addiction ( Baturay and Toker, 2019 ). Therefore, parents should assume a proper perspective on the rationality of gaming and adopt the right approach to guide their children.

One key factor influencing the quality of higher education is students’ learning engagement. The integration of educational information technology has disrupted traditional teaching methods. This trend has accelerated in the context of COVID-19. College students’ growth mindset can impact their learning engagement through the role of the perceived COVID-19 event strength and perceived stress ( Zhao et al., 2021 ). Moreover, students’ self-regulated learning and social presence positively affect their learning engagement in online contexts ( Miao and Ma, 2022 ). Students’ liking of the teacher positively affects their learning engagement ( Lu et al., 2022 ). Their perceived teacher support also positively affects their learning engagement ( An et al., 2022 ). Hence, educators should focus on teacher support and care in the teaching and learning process.

Students’ motivation for academic achievement can often be influenced by active interventions. Cheng et al. (2022) noted that the cumulative process of students gaining successful experiences contributed to an increased sense of self-efficacy, motivating them to learn. Zhou (2009) illustrated that cooperative learning motivated students’ academic achievement. In addition, Hong J. C. et al. (2021) showed that poor parent–child relationships (such as the behavior of “mama’ s boy” in adults) had a negative impact on students’ academic achievement motivation, and they concluded that cell phone addiction was more pronounced among students with low academic achievement motivation. Hence, enhancing students’ academic achievement motivation also requires family support.

5.3. Research limitations and suggestions for future research

Most of the past studies on the impact of online game addiction on academics have used quantitative research as the research method. The qualitative research approach regarding students’ online game addiction should not be neglected. By collecting objective factual materials in the form of qualitative research such as interviews a greater understanding of students’ actual views on games and the psychological factors of addiction can be achieved. Therefore, future studies could introduce more qualitative research to study online game addiction.

To pay attention to the problem of students’ online game addiction, universities and families should not wait until they become addicted and try to remedy it, but should start to prevent it before it gets to that stage. In terms of developing students’ personal psychological qualities, students’ sensation-seeking and loneliness can significantly affect their tendency to become addicted to online games ( Batmaz and Çelik, 2021 ). Adolescents’ pain intolerance problems can also contribute to Internet overuse ( Gu, 2022 ). Emotion-regulation methods affect the emotional experience and play a vital role in Internet addiction ( Liang et al., 2021 ). In this regard, it is necessary to pay attention to students’ mental health status and to guide them to establish correct values and pursue goals through psychological guidance and other means.

In addition to individual factors, different parenting can considerably impact adolescents. Adolescents who tend to experience more developmental assets are less likely to develop IGD ( Xiang et al., 2022a ), and external resources can facilitate the development of internal resources, discouraging adolescents from engaging in IGD ( Xiang et al., 2022b ). Relevant research indicates that the most critical factor in adolescents’ game addiction tendency comes from society or their parents rather than being the adolescents’ fault ( Choi et al., 2018 ). Adolescents who tend to be addicted to online games may have discordant parent–child relationships ( Eliseeva and Krieger, 2021 ). Better father-child and mother–child relationships predict lower initial levels of Internet addiction in adolescents ( Shek et al., 2019 ). Family-based approaches such as improved parent–child relationships and increased communication and understanding among family members can be a direction for adolescent Internet addiction prevention ( Yu and Shek, 2013 ).

At the school level, a close teacher-student relationship is one of the main factors influencing students’ psychological state. Students’ participation in and control over the teaching and learning process as well as their closeness to teachers can increase their satisfaction and thus enhance their learning-related well-being ( Yang J. et al., 2021 ). More school resources can lead to higher adolescent self-control, attenuating students’ online gaming disorders ( Xiang et al., 2022c ).

Data availability statement

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

Ethics statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. Written informed consent was not obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Author contributions

R-QS, and J-HY: concept and design and drafting of the manuscript. R-QS, and J-HY: acquisition of data and statistical analysis. G-FS, and J-HY: critical revision of the manuscript. All authors contributed to the article and approved the submitted version.

This work was supported by Beijing Normal University First-Class Discipline Cultivation Project for Educational Science (Grant number: YLXKPY-XSDW202211). The Project Name is “Research on Theoretical Innovation and Institutional System of Promoting the Modernization of Vocational Education with Modern Chinese Characteristics”.

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.

Publisher’s note

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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Keywords: college students, online game addiction, learning engagement, reduced academic achievement motivation, online games

Citation: Sun R-Q, Sun G-F and Ye J-H (2023) The effects of online game addiction on reduced academic achievement motivation among Chinese college students: the mediating role of learning engagement. Front. Psychol . 14:1185353. doi: 10.3389/fpsyg.2023.1185353

Received: 13 March 2023; Accepted: 08 June 2023; Published: 13 July 2023.

Reviewed by:

Copyright © 2023 Sun, Sun and Ye. 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: Jian-Hong Ye, [email protected]

† These authors have contributed equally to this work and share first authorship

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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Digital addiction: systematic review of computer game addiction impact on adolescent physical health.

1. Introduction

2. systematic planning, research questions, 3. methodology, 3.1. focus group discussion, 3.1.1. group 1: medical experts, 3.1.2. group 2: professional game experts, 3.2. literature search, 3.2.1. the review protocol, 3.2.2. database and selection criteria, 3.2.3. search strategy, 3.2.4. publication selection, 3.2.5. data extraction, 3.2.6. risk of bias across studies, 4. results and discussions, 4.1. rq1: digital addiction (da), 4.1.1. rq1.0: what is addiction, 4.1.2. rq1.1: what is da, 4.1.3. rq1.2: what are the causes of da.

• Achievers—always aim to achieve the goals set in the computer game (such as ranking higher in levels, reputations, and collection of treasure).
• Explorers—players are primarily interested in the study of the environment of the simulated world (such as geography and physics).
• Socializers—are interested in interacting with another player—either to impose themselves or to promote themselves.
• Killers—keep their interaction alive with other players—they keep communication and role-play active for teamwork.

4.1.4. RQ1.3: How Does DA Impact an Addict?

4.1.5. rq1.4: how does the withdrawal of the addictive substance impact an addict, 4.2. rq2: internet gaming disorder (igd), 4.2.1. rq2.0: what is igd, 4.2.2. rq2.1: what are the causes of computer game addiction, 4.2.3. rq2.2: what are the effects of excessive computer gaming/igd, 4.2.4. rq2.3: is igd diagnosable and curable, 4.3. rq3: what are the factors of da in computer games which influence malaysian adolescents.

• Time management—most computer gamers tend to spend extensive hours playing computer games, and they often spend late nights online with their gaming community. This situation raises concerns, as spending too much time on computer games affects the gamer’s personal and professional life as a result.
• Social life—social life is related to the relationship of the individual with family, friends, and their surrounding community. The five DA components related to the DA factor of social life will become a part of the personal lives of gamers. For instance, salience causes gamers to consider playing a game as an essential part of life. Mood modification lets gamers have mood swings and tend to spend more time playing games in their room. Relapse causes gaming behavior to become an addiction and keep repeating the gaming sessions. Harm causes gamers to think and behave aggressively with people around them, as aggression is a part of a computer game—MMOGs. Conflict is a situation where gamers challenge each other online, which, if brought into real-life, may cause harm and danger to other people.
• Psychological and behavior—Physical and behavioral components of addiction include physical health, loss of control, and withdrawal. Physical health, as mentioned before, is a situation where gamers have issues with their health physically, such as neck and back pain. Loss of control includes mood swings, and withdrawal is the behavior changes of the addict when they are withdrawn from the addictive substance.

4.4. RQ4: What Are the Components of DA in Computer Games Which Influence Malaysian Adolescents?

4.5. rq5: what are the consequences of computer game addiction on adolescent physical health.

• Obesity—computer games addiction may cause adolescents to gain weight and become obese as gamers tend to continue eating while playing computer games, and at the same time, have no active physical movement to burn the added calories.
• Back pain and neck pain—an extensive computer gaming period may cause gamers to have back and neck pain, as they tend to sit in the same position for hours while playing computer games.
• Orthopaedic/joint muscle—Some might have orthopedic/joint problems, called gamer’s thumb, or hand injuries due to spending an excessive amount of time using a mouse and keyboard.
• Eyesight problems—excessive computer gaming and the use of screens negatively impact eyesight. A study by Lee et al. [ 54 ] has specifically focused on the effect of excessive computer gaming on binocular vision. The result suggests that excessive and constant gaming activity on computers causes both the weakening of visual functions and ocular and physical fatigue.
• Hearing problems—computer gamers may also have reduced hearing ability, as they are used to listening to loud noises using their headphones. Some of the noises include loud sound effects, such as shooting, explosions, engines roaring, and other loud sound effects that are designed to immerse gamers into the gaming world.
• Physical inactivity—computer gamers tend to spend more time playing computer games in a room instead of going for outdoor activities.

5. Discussion

6. conclusions, author contributions, informed consent statement, conflicts of interest.

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Aziz, N.; Nordin, M.J.; Abdulkadir, S.J.; Salih, M.M.M. Digital Addiction: Systematic Review of Computer Game Addiction Impact on Adolescent Physical Health. Electronics 2021 , 10 , 996. https://doi.org/10.3390/electronics10090996

Aziz N, Nordin MJ, Abdulkadir SJ, Salih MMM. Digital Addiction: Systematic Review of Computer Game Addiction Impact on Adolescent Physical Health. Electronics . 2021; 10(9):996. https://doi.org/10.3390/electronics10090996

Aziz, Norshakirah, Md Jan Nordin, Said Jadid Abdulkadir, and Muhammad Muhaimin M. Salih. 2021. "Digital Addiction: Systematic Review of Computer Game Addiction Impact on Adolescent Physical Health" Electronics 10, no. 9: 996. https://doi.org/10.3390/electronics10090996

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Internet Addiction: A Brief Summary of Research and Practice

Hilarie cash.

a reSTART Internet Addiction Recovery Program, Fall City, WA 98024

Cosette D Rae

Ann h steel, alexander winkler.

b University of Marburg, Department for Clinical Psychology and Psychotherapy, Gutenbergstraße 18, 35032 Marburg, Germany

Problematic computer use is a growing social issue which is being debated worldwide. Internet Addiction Disorder (IAD) ruins lives by causing neurological complications, psychological disturbances, and social problems. Surveys in the United States and Europe have indicated alarming prevalence rates between 1.5 and 8.2% [1]. There are several reviews addressing the definition, classification, assessment, epidemiology, and co-morbidity of IAD [2-5], and some reviews [6-8] addressing the treatment of IAD. The aim of this paper is to give a preferably brief overview of research on IAD and theoretical considerations from a practical perspective based on years of daily work with clients suffering from Internet addiction. Furthermore, with this paper we intend to bring in practical experience in the debate about the eventual inclusion of IAD in the next version of the Diagnostic and Statistical Manual of Mental Disorders (DSM).

INTRODUCTION

The idea that problematic computer use meets criteria for an addiction, and therefore should be included in the next iteration of the Diagnostic and Statistical Manual of Mental Disorders (DSM) , 4 th ed. Text Revision [ 9 ] was first proposed by Kimberly Young, PhD in her seminal 1996 paper [ 10 ]. Since that time IAD has been extensively studied and is indeed, currently under consideration for inclusion in the DSM-V [ 11 ]. Meanwhile, both China and South Korea have identified Internet addiction as a significant public health threat and both countries support education, research and treatment [ 12 ]. In the United States, despite a growing body of research, and treatment for the disorder available in out-patient and in-patient settings, there has been no formal governmental response to the issue of Internet addiction. While the debate goes on about whether or not the DSM-V should designate Internet addiction a mental disorder [ 12 - 14 ] people currently suffering from Internet addiction are seeking treatment. Because of our experience we support the development of uniform diagnostic criteria and the inclusion of IAD in the DSM-V [ 11 ] in order to advance public education, diagnosis and treatment of this important disorder.

CLASSIFICATION

There is ongoing debate about how best to classify the behavior which is characterized by many hours spent in non-work technology-related computer/Internet/video game activities [ 15 ]. It is accompanied by changes in mood, preoccupation with the Internet and digital media, the inability to control the amount of time spent interfacing with digital technology, the need for more time or a new game to achieve a desired mood, withdrawal symptoms when not engaged, and a continuation of the behavior despite family conflict, a diminishing social life and adverse work or academic consequences [ 2 , 16 , 17 ]. Some researchers and mental health practitioners see excessive Internet use as a symptom of another disorder such as anxiety or depression rather than a separate entity [e.g. 18]. Internet addiction could be considered an Impulse control disorder (not otherwise specified). Yet there is a growing consensus that this constellation of symptoms is an addiction [e.g. 19]. The American Society of Addiction Medicine (ASAM) recently released a new definition of addiction as a chronic brain disorder, officially proposing for the first time that addiction is not limited to substance use [ 20 ]. All addictions, whether chemical or behavioral, share certain characteristics including salience, compulsive use (loss of control), mood modification and the alleviation of distress, tolerance and withdrawal, and the continuation despite negative consequences.

DIAGNOSTIC CRITERIA FOR IAD

The first serious proposal for diagnostic criteria was advanced in 1996 by Dr. Young, modifying the DSM-IV criteria for pathological gambling [ 10 ]. Since then variations in both name and criteria have been put forward to capture the problem, which is now most popularly known as Internet Addiction Disorder. Problematic Internet Use (PIU) [ 21 ], computer addiction, Internet dependence [ 22 ], compulsive Internet use, pathological Internet use [ 23 ], and many other labels can be found in the literature. Likewise a variety of often overlapping criteria have been proposed and studied, some of which have been validated. However, empirical studies provide an inconsistent set of criteria to define Internet addiction [ 24 ]. For an overview see Byun et al . [ 25 ].

Beard [ 2 ] recommends that the following five diagnostic criteria are required for a diagnosis of Internet addiction: (1) Is preoccupied with the Internet (thinks about previous online activity or anticipate next online session); (2) Needs to use the Internet with increased amounts of time in order to achieve satisfaction; (3) Has made unsuccessful efforts to control, cut back, or stop Internet use; (4) Is restless, moody, depressed, or irritable when attempting to cut down or stop Internet use; (5) Has stayed online longer than originally intended. Additionally, at least one of the following must be present: (6) Has jeopardized or risked the loss of a significant relationship, job, educational or career opportunity because of the Internet; (7) Has lied to family members, therapist, or others to conceal the extent of involvement with the Internet; (8) Uses the Internet as a way of escaping from problems or of relieving a dysphoric mood (e.g., feelings of helplessness, guilt, anxiety, depression) [ 2 ].

There has been also been a variety of assessment tools used in evaluation. Young’s Internet Addiction Test [ 16 ], the Problematic Internet Use Questionnaire (PIUQ) developed by Demetrovics, Szeredi, and Pozsa [ 26 ] and the Compulsive Internet Use Scale (CIUS) [ 27 ] are all examples of instruments to assess for this disorder.

The considerable variance of the prevalence rates reported for IAD (between 0.3% and 38%) [ 28 ] may be attributable to the fact that diagnostic criteria and assessment questionnaires used for diagnosis vary between countries and studies often use highly selective samples of online surveys [ 7 ]. In their review Weinstein and Lejoyeux [ 1 ] report that surveys in the United States and Europe have indicated prevalence rates varying between 1.5% and 8.2%. Other reports place the rates between 6% and 18.5% [ 29 ].

“Some obvious differences with respect to the methodologies, cultural factors, outcomes and assessment tools forming the basis for these prevalence rates notwithstanding, the rates we encountered were generally high and sometimes alarming.” [ 24 ]

There are different models available for the development and maintenance of IAD like the cognitive-behavioral model of problematic Internet use [ 21 ], the anonymity, convenience and escape (ACE) model [ 30 ], the access, affordability, anonymity (Triple-A) engine [ 31 ], a phases model of pathological Internet use by Grohol [ 32 ], and a comprehensive model of the development and maintenance of Internet addiction by Winkler & Dörsing [ 24 ], which takes into account socio-cultural factors ( e.g. , demographic factors, access to and acceptance of the Internet), biological vulnerabilities ( e.g. , genetic factors, abnormalities in neurochemical processes), psychological predispositions ( e.g. , personality characteristics, negative affects), and specific attributes of the Internet to explain “excessive engagement in Internet activities” [ 24 ].

NEUROBIOLOGICAL VULNERABILITIES

It is known that addictions activate a combination of sites in the brain associated with pleasure, known together as the “reward center” or “pleasure pathway” of the brain [ 33 , 34 ]. When activated, dopamine release is increased, along with opiates and other neurochemicals. Over time, the associated receptors may be affected, producing tolerance or the need for increasing stimulation of the reward center to produce a “high” and the subsequent characteristic behavior patterns needed to avoid withdrawal. Internet use may also lead specifically to dopamine release in the nucleus accumbens [ 35 , 36 ], one of the reward structures of the brain specifically involved in other addictions [ 20 ]. An example of the rewarding nature of digital technology use may be captured in the following statement by a 21 year-old male in treatment for IAD:

“I feel technology has brought so much joy into my life. No other activity relaxes me or stimulates me like technology. However, when depression hits, I tend to use technology as a way of retreating and isolating.”

REINFORCEMENT/REWARD

What is so rewarding about Internet and video game use that it could become an addiction? The theory is that digital technology users experience multiple layers of reward when they use various computer applications. The Internet functions on a variable ratio reinforcement schedule (VRRS), as does gambling [ 29 ]. Whatever the application (general surfing, pornography, chat rooms, message boards, social networking sites, video games, email, texting, cloud applications and games, etc.), these activities support unpredictable and variable reward structures. The reward experienced is intensified when combined with mood enhancing/stimulating content. Examples of this would be pornography (sexual stimulation), video games (e.g. various social rewards, identification with a hero, immersive graphics), dating sites (romantic fantasy), online poker (financial) and special interest chat rooms or message boards (sense of belonging) [ 29 , 37 ].

BIOLOGICAL PREDISPOSITION

There is increasing evidence that there can be a genetic predisposition to addictive behaviors [ 38 , 39 ]. The theory is that individuals with this predisposition do not have an adequate number of dopamine receptors or have an insufficient amount of serotonin/dopamine [ 2 ], thereby having difficulty experiencing normal levels of pleasure in activities that most people would find rewarding. To increase pleasure, these individuals are more likely to seek greater than average engagement in behaviors that stimulate an increase in dopamine, effectively giving them more reward but placing them at higher risk for addiction.

MENTAL HEALTH VULNERABILITIES

Many researchers and clinicians have noted that a variety of mental disorders co-occur with IAD. There is debate about which came first, the addiction or the co-occurring disorder [ 18 , 40 ]. The study by Dong et al . [ 40 ] had at least the potential to clarify this question, reporting that higher scores for depression, anxiety, hostility, interpersonal sensitivity, and psychoticism were consequences of IAD. But due to the limitations of the study further research is necessary.

THE TREATMENT OF INTERNET ADDICTION

There is a general consensus that total abstinence from the Internet should not be the goal of the interventions and that instead, an abstinence from problematic applications and a controlled and balanced Internet usage should be achieved [ 6 ]. The following paragraphs illustrate the various treatment options for IAD that exist today. Unless studies examining the efficacy of the illustrated treatments are not available, findings on the efficacy of the presented treatments are also provided. Unfortunately, most of the treatment studies were of low methodological quality and used an intra-group design.

The general lack of treatment studies notwithstanding, there are treatment guidelines reported by clinicians working in the field of IAD. In her book “Internet Addiction: Symptoms, Evaluation, and Treatment”, Young [ 41 ] offers some treatment strategies which are already known from the cognitive-behavioral approach: (a) practice opposite time of Internet use (discover patient’s patterns of Internet use and disrupt these patterns by suggesting new schedules), (b) use external stoppers (real events or activities prompting the patient to log off), (c) set goals (with regard to the amount of time), (d) abstain from a particular application (that the client is unable to control), (e) use reminder cards (cues that remind the patient of the costs of IAD and benefits of breaking it), (f) develop a personal inventory (shows all the activities that the patient used to engage in or can’t find the time due to IAD), (g) enter a support group (compensates for a lack of social support), and (h) engage in family therapy (addresses relational problems in the family) [ 41 ]. Unfortunately, clinical evidence for the efficacy of these strategies is not mentioned.

Non-psychological Approaches

Some authors examine pharmacological interventions for IAD, perhaps due to the fact that clinicians use psychopharmacology to treat IAD despite the lack of treatment studies addressing the efficacy of pharmacological treatments. In particular, selective serotonin-reuptake inhibitors (SSRIs) have been used because of the co-morbid psychiatric symptoms of IAD (e.g. depression and anxiety) for which SSRIs have been found to be effective [ 42 - 46 ]. Escitalopram (a SSRI) was used by Dell’Osso et al . [ 47 ] to treat 14 subjects with impulsive-compulsive Internet usage disorder. Internet usage decreased significantly from a mean of 36.8 hours/week to a baseline of 16.5 hours/week. In another study Han, Hwang, and Renshaw [ 48 ] used bupropion (a non-tricyclic antidepressant) and found a decrease of craving for Internet video game play, total game play time, and cue-induced brain activity in dorsolateral prefrontal cortex after a six week period of bupropion sustained release treatment. Methylphenidate (a psycho stimulant drug) was used by Han et al . [ 49 ] to treat 62 Internet video game-playing children diagnosed with attention-deficit hyperactivity disorder. After eight weeks of treatment, the YIAS-K scores and Internet usage times were significantly reduced and the authors cautiously suggest that methylphenidate might be evaluated as a potential treatment of IAD. According to a study by Shapira et al . [ 50 ], mood stabilizers might also improve the symptoms of IAD. In addition to these studies, there are some case reports of patients treated with escitalopram [ 45 ], citalopram (SSRI)- quetiapine (antipsychotic) combination [ 43 ] and naltrexone (an opioid receptor antagonist) [ 51 ].

A few authors mentioned that physical exercise could compensate the decrease of the dopamine level due to decreased online usage [ 52 ]. In addition, sports exercise prescriptions used in the course of cognitive behavioral group therapy may enhance the effect of the intervention for IAD [ 53 ].

Psychological Approaches

Motivational interviewing (MI) is a client-centered yet directive method for enhancing intrinsic motivation to change by exploring and resolving client ambivalence [ 54 ]. It was developed to help individuals give up addictive behaviors and learn new behavioral skills, using techniques such as open-ended questions, reflective listening, affirmation, and summarization to help individuals express their concerns about change [ 55 ]. Unfortunately, there are currently no studies addressing the efficacy of MI in treating IAD, but MI seems to be moderately effective in the areas of alcohol, drug addiction, and diet/exercise problems [ 56 ].

Peukert et al . [ 7 ] suggest that interventions with family members or other relatives like “Community Reinforcement and Family Training” [ 57 ] could be useful in enhancing the motivation of an addict to cut back on Internet use, although the reviewers remark that control studies with relatives do not exist to date.

Reality therapy (RT) is supposed to encourage individuals to choose to improve their lives by committing to change their behavior. It includes sessions to show clients that addiction is a choice and to give them training in time management; it also introduces alternative activities to the problematic behavior [ 58 ]. According to Kim [ 58 ], RT is a core addiction recovery tool that offers a wide variety of uses as a treatment for addictive disorders such as drugs, sex, food, and works as well for the Internet. In his RT group counseling program treatment study, Kim [ 59 ] found that the treatment program effectively reduced addiction level and improved self-esteem of 25 Internet-addicted university students in Korea.

Twohig and Crosby [ 60 ] used an Acceptance & Commitment Therapy (ACT) protocol including several exercises adjusted to better fit the issues with which the sample struggles to treat six adult males suffering from problematic Internet pornography viewing. The treatment resulted in an 85% reduction in viewing at post-treatment with results being maintained at the three month follow-up (83% reduction in viewing pornography).

Widyanto and Griffith [ 8 ] report that most of the treatments employed so far had utilized a cognitive-behavioral approach. The case for using cognitive-behavioral therapy (CBT) is justified due to the good results in the treatment of other behavioral addictions/impulse-control disorders, such as pathological gambling, compulsive shopping, bulimia nervosa, and binge eating-disorders [ 61 ]. Wölfling [ 5 ] described a predominantly behavioral group treatment including identification of sustaining conditions, establishing of intrinsic motivation to reduce the amount of time being online, learning alternative behaviors, engagement in new social real-life contacts, psycho-education and exposure therapy, but unfortunately clinical evidence for the efficacy of these strategies is not mentioned. In her study, Young [ 62 ] used CBT to treat 114 clients suffering from IAD and found that participants were better able to manage their presenting problems post-treatment, showing improved motivation to stop abusing the Internet, improved ability to control their computer use, improved ability to function in offline relationships, improved ability to abstain from sexually explicit online material, improved ability to engage in offline activities, and improved ability to achieve sobriety from problematic applications. Cao, Su and Gao [ 63 ] investigated the effect of group CBT on 29 middle school students with IAD and found that IAD scores of the experimental group were lower than of the control group after treatment. The authors also reported improvement in psychological function. Thirty-eight adolescents with IAD were treated with CBT designed particularly for addicted adolescents by Li and Dai [ 64 ]. They found that CBT has good effects on the adolescents with IAD (CIAS scores in the therapy group were significant lower than that in the control group). In the experimental group the scores of depression, anxiety, compulsiveness, self-blame, illusion, and retreat were significantly decreased after treatment. Zhu, Jin, and Zhong [ 65 ] compared CBT and electro acupuncture (EA) plus CBT assigning forty-seven patients with IAD to one of the two groups respectively. The authors found that CBT alone or combined with EA can significantly reduce the score of IAD and anxiety on a self-rating scale and improve self-conscious health status in patients with IAD, but the effect obtained by the combined therapy was better.

Multimodal Treatments

A multimodal treatment approach is characterized by the implementation of several different types of treatment in some cases even from different disciplines such as pharmacology, psychotherapy and family counseling simultaneously or sequentially. Orzack and Orzack [ 66 ] mentioned that treatments for IAD need to be multidisciplinary including CBT, psychotropic medication, family therapy, and case managers, because of the complexity of these patients’ problems.

In their treatment study, Du, Jiang, and Vance [ 67 ] found that multimodal school-based group CBT (including parent training, teacher education, and group CBT) was effective for adolescents with IAD (n = 23), particularly in improving emotional state and regulation ability, behavioral and self-management style. The effect of another multimodal intervention consisting of solution-focused brief therapy (SFBT), family therapy, and CT was investigated among 52 adolescents with IAD in China. After three months of treatment, the scores on an IAD scale (IAD-DQ), the scores on the SCL-90, and the amount of time spent online decreased significantly [ 68 ]. Orzack et al . [ 69 ] used a psychoeducational program, which combines psychodynamic and cognitive-behavioral theoretical perspectives, using a combination of Readiness to Change (RtC), CBT and MI interventions to treat a group of 35 men involved in problematic Internet-enabled sexual behavior (IESB). In this group treatment, the quality of life increased and the level of depressive symptoms decreased after 16 (weekly) treatment sessions, but the level of problematic Internet use failed to decrease significantly [ 69 ]. Internet addiction related symptom scores significantly decreased after a group of 23 middle school students with IAD were treated with Behavioral Therapy (BT) or CT, detoxification treatment, psychosocial rehabilitation, personality modeling and parent training [ 70 ]. Therefore, the authors concluded that psychotherapy, in particular CT and BT were effective in treating middle school students with IAD. Shek, Tang, and Lo [ 71 ] described a multi-level counseling program designed for young people with IAD based on the responses of 59 clients. Findings of this study suggest this multi-level counseling program (including counseling, MI, family perspective, case work and group work) is promising to help young people with IAD. Internet addiction symptom scores significantly decreased, but the program failed to increase psychological well-being significantly. A six-week group counseling program (including CBT, social competence training, training of self-control strategies and training of communication skills) was shown to be effective on 24 Internet-addicted college students in China [ 72 ]. The authors reported that the adapted CIAS-R scores of the experimental group were significantly lower than those of the control group post-treatment.

The reSTART Program

The authors of this article are currently, or have been, affiliated with the reSTART: Internet Addiction Recovery Program [ 73 ] in Fall City, Washington. The reSTART program is an inpatient Internet addiction recovery program which integrates technology detoxification (no technology for 45 to 90 days), drug and alcohol treatment, 12 step work, cognitive behavioral therapy (CBT), experiential adventure based therapy, Acceptance and Commitment therapy (ACT), brain enhancing interventions, animal assisted therapy, motivational interviewing (MI), mindfulness based relapse prevention (MBRP), Mindfulness based stress reduction (MBSR), interpersonal group psychotherapy, individual psychotherapy, individualized treatments for co-occurring disorders, psycho- educational groups (life visioning, addiction education, communication and assertiveness training, social skills, life skills, Life balance plan), aftercare treatments (monitoring of technology use, ongoing psychotherapy and group work), and continuing care (outpatient treatment) in an individualized, holistic approach.

The first results from an ongoing OQ45.2 [ 74 ] study (a self-reported measurement of subjective discomfort, interpersonal relationships and social role performance assessed on a weekly basis) of the short-term impact on 19 adults who complete the 45+ days program showed an improved score after treatment. Seventy-four percent of participants showed significant clinical improvement, 21% of participants showed no reliable change, and 5% deteriorated. The results have to be regarded as preliminary due to the small study sample, the self-report measurement and the lack of a control group. Despite these limitations, there is evidence that the program is responsible for most of the improvements demonstrated.

As can be seen from this brief review, the field of Internet addiction is advancing rapidly even without its official recognition as a separate and distinct behavioral addiction and with continuing disagreement over diagnostic criteria. The ongoing debate whether IAD should be classified as an (behavioral) addiction, an impulse-control disorder or even an obsessive compulsive disorder cannot be satisfactorily resolved in this paper. But the symptoms we observed in clinical practice show a great deal of overlap with the symptoms commonly associated with (behavioral) addictions. Also it remains unclear to this day whether the underlying mechanisms responsible for the addictive behavior are the same in different types of IAD (e.g., online sexual addiction, online gaming, and excessive surfing). From our practical perspective the different shapes of IAD fit in one category, due to various Internet specific commonalities (e.g., anonymity, riskless interaction), commonalities in the underlying behavior (e.g., avoidance, fear, pleasure, entertainment) and overlapping symptoms (e.g., the increased amount of time spent online, preoccupation and other signs of addiction). Nevertheless more research has to be done to substantiate our clinical impression.

Despite several methodological limitations, the strength of this work in comparison to other reviews in the international body of literature addressing the definition, classification, assessment, epidemiology, and co-morbidity of IAD [ 2 - 5 ], and to reviews [ 6 - 8 ] addressing the treatment of IAD, is that it connects theoretical considerations with the clinical practice of interdisciplinary mental health experts working for years in the field of Internet addiction. Furthermore, the current work gives a good overview of the current state of research in the field of internet addiction treatment. Despite the limitations stated above this work gives a brief overview of the current state of research on IAD from a practical perspective and can therefore be seen as an important and helpful paper for further research as well as for clinical practice in particular.

ACKNOWLEDGEMENTS

Declared none.

CONFLICT OF INTEREST

The authors confirm that this article content has no conflict of interest.

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A Qualitative Study on the Cause and Effect of Gaming Addiction Among Senior High School Students Of Universidad de Manila

Technology has come a long way. With the invention of computers, cellular phones, and the Internet, it is inevitable to avoid gaming with the use of these modern devices. Most people tend to use their vacant hours on playing online/video games for fun and entertainment. Others are freeing their time from loads of paper and legwork. Specifically, those students who are piled with school works and needing a break. They tend to engage themselves in online/video gaming and this may lead to gaming addiction. Many ideas point to the significant effects of gaming. Therefore, it is viewed that we must know why gaming is addictive and how does it affect a student in Senior High School. The main objective of this research is to find the causes of gaming addiction and its effect on the SHS students. Knowing the reasons and the effects of gaming addiction can easily help the students manage their time, to be more accountable in school and to focus on their studies.

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James Ryan Paredes

Online video games are video games played online via a mobile device, and are particularly popular when downloaded for free (e.g., “freemium game” – games played for free and where customers can pay for extra features), and can be single-player or multiplayer games (Su, Chiang, Lee, & Chang, 2016). It is also one of the main entertainment features on smartphones, and this could be potentially problematic in terms of dependent use among the students. Moreover, the purpose of this study is to enlighten the students and increase consciousness regarding the effect of mobile game addiction and to advocate prevention towards this problem. Comprehending the gathered data, the researcher decided to conduct this study to find out the effect on the students. In addition, it also aims to suggest possible solutions and preventions to the rapidly increasing of too much usage of online video games that may lead to addiction.

Galang Surya Gumilang

This study aims to determine: (1) online game addiction of junior high school students in Kediri City, (2) junior high school students' learning achievement, (3) and the relationship between online game addiction and learning achievement. Mixed Methods Design is used as a combination or combination of quantitative and qualitative research approaches including qualitative and quantitative data in a single study. The strategy used, namely the Congruent Embedded Strategy, is an interesting research strategy, namely in one stage of data collection, researchers are able to collect two types of data together. The results of the quantitative research show that students who are addicted to online games have 3 students (16.03%) including very high, 4 students (14.36%) including high, 13 students (41.86%) including moderate, 4 students (11.9%) is low, and 5 students (12.66%) is very low. In addition, there are 12 students who have low learning achievement, namely getting the "less" and "very poor" categories so that they get an unsatisfactory ranking. While there are 6 students who get the "enough" category and 12 other students get the "very good" and "good" categories. From these results, there is no significant relationship between online game addiction and learning achievement because the value of the hypothesis test is 0.291. On the qualitative side, from 30 students it was found that (1) His early playing online games, (2) The average time spent playing 2-4 hours, (3) His parents did not allow him to play online games, and (4) Achievement low learning.

International Journal of English Literature and Social Sciences

Bren Bondoc

Purna P . Chapagai

In Bhutan, the popularity of the video games picked up over the years and with the advent of mobile phones, it has only added to the gaming culture in the country. This research was conducted in order determine the factors causing students to play online games, effect of engaging in online games on academic performances and evaluate the effect of engaging in online games on mental health. The research was conducted at college of natural resources (CNR), located in Lobesa, Punakha. Random sampling method and Slovian's formula was used to determine the sample size. The data was collected through a semi-structured questionnaire distributed through google docs. The data was stored in Microsoft Excel and was analyzed using Statistical Package for Social Science (SPSS) version 25. Regression, ANOVA and Chisquare Test was conducted to find out the association between various variable. A total of 231 respondents took part in this survey of which 56.7% were female and 43.3% were male. More than half of the respondents were actively engaged in online games. Pearson's Chi square test revealed that there was no association between playing online games and gender. The academic performance of the respondents revealed that 3.03% were below 60%, 32.46% were between 60-70%, 53.67% between 70-80% and 10.82% of the respondents were above 80%. Chi square test revealed that significant association existed between playing online games and academic performance (p<0.05). Significant association was also discovered between academic performance and duration of online games played in a week. Online gaming can be considered as the two side of a coin, it can be advantageous as well as disadvantageous. The disadvantages of engaging in online games was the decreased time for social activities, negative impact on academic performances, aggression and frustration and leads to addiction. Majority of the respondents agreed that playing online games will lead addiction and Pearson chi square test revealed that the amount of time spent on online games was significantly associated with addiction. On the other hand, the advantages of engaging in online game is to reduce the venerability to anxiety and depression, and enhances creativity and imagination. Majority of the respondents disagreed that playing online games helps them improve their academic performances, while agreeing that online games help them to boost creativity and makes them more imaginative. There are several factors that motivated the respondents to engage in online games, majority of the respondents played due to their friends.

GLOBUS JOURNAL OF PROGRESSIVE EDUCATION

Janeth Falcunit

Computer games are the most popular form of entertainment in modern societies today. The researcher observed that there is a problem regarding excess and unhealthy use of playing computers. Based on these observations, the researcher is motivated to conduct a study entitled “Gameaholics: A Closer Look into the Phenomenon of Computer Addiction.” This study highlighted the reasons behind the phenomenon of computer game addiction among the students of Central Philippines State University (CPSU)-Hinigaran Campus for the academic year 2020-2021. In order for the researcher to know the reasons behind the phenomenon of computer game addiction among the students of CPSU- Hinigaran Campus, the qualitative research design was employed since the objective of this study was to know the reasons behind computer addiction. Moreover, this study applied purposive sampling design since the participants were selected purposively through the set criteria. Specifically, the researcher used one-on-one interview in collaborating, understanding, and investigating the participants’ experiences. Interviews were audio recorded. Thematic approach was used in data analysis. Through thematic analysis, it was found that Spirited: Race to the Top and Escapism: A Shelter from the Storm, Cut-and- Dried, Eyes on the Prize, Thrill and Challenge: Invitation to Addiction were the common theme formed in relation to the objective of this study. Further, the researcher also discovered an additional theme, Fatigue; What I feel, which focused on Physical Exhaustion. This theme surfaced as the participants expressed their reason why they were hooked in computer games too much. Keywords: Computer, Computer Game Addiction, Gameaholics.

KnE Social Sciences

Dennis Dumrique

International Journal of Engineering Technology Research & Management (ijetrm)

Ijetrm Journal , Maheshwari Ecom

Online game addiction has become a common phenomenon that affects many young minds. In this study we rely on the problems faced by the adolescents after addicted to online games. This research is totally based on online gaming addiction on which the survey has been taken in Coimbatore city particularly with respect to school students. The main sampling technique used in this research with equal distribution of data from high school and higher secondary school students is quota. Information’s of this study are collected from both primary and secondary data. Research duration of this study are from June to September 2019. This study concludes that even though online games have created a new evolution , it also has created a great physical and mental impact on students addiction.

World Journal on Educational Technology

WJET Journal , Aşkım KURT

The computer gaming addiction is one of the newer concepts that young generations face and can be defined as the excessive and problematic use of computer games leading to social and/or emotional problems. The purpose of this study is to analyse through variables the computer gaming addiction levels of secondary school students. The research was conducted with survey and causal-comparative quantitative research methods. Furthermore, the quantitative data was obtained by interpreting the data obtained through open-ended questions. Findings reveal a significant difference between computer gaming addiction and variables of gender, daily gaming times and whether or not students play games with people they do not know. However, findings did not show any significant difference between computer gaming addiction and variables of grade or purposes of game playing. According to the findings from qualitative data analysis, students mostly prefer to play skill-based games, while they would want to design action games.

Addicta: The Turkish Journal on Addiction

ADDİCTA Dergisi , Eyüp YILMAZ

Video gaming has now become very popular among children and adolescents. Because of the increasing use of videogames, the debate concerning the effects of video games has been ongoing, particularly in terms of gamers' social lives. This study explores the impact of heavy gaming students on their peers and teachers in the school environment. For this purpose, focus-group and face-to-face interviews have been carried out using semi-structured interview forms developed by the research team. The data have been collected from 21 participants comprised of three heavy gaming students, 16 peers, and two teachers. The findings indicate heavy gamers to display problematic behaviors including communication and behavioral problems within the school environment. Results also show heavy gaming students to prefer staying at home and playing videogames rather than attending school activities. Heavy gamers mostly prefer spending time with other heavy gamers or with male peers because they have mutual topics they can talk about (video games, their game talents, soccer, outdoor games etc.), which they are unable to with girls. According to the teachers, heavy gamers have low-school performance. However, the English teacher emphasized the positive effects of video gaming on students' English vocabulary.

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