computer games addiction essay

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• Published: 28 November 2022

Psychological treatments for excessive gaming: a systematic review and meta-analysis

• Jueun Kim 1 ,
• Sunmin Lee 1 ,
• Dojin Lee 1 ,
• Sungryul Shim 2 ,
• Daniel Balva 3 ,
• Kee-Hong Choi 4 ,
• Jeanyung Chey 5 ,
• Suk-Ho Shin 6 &
• Woo-Young Ahn 5  

Scientific Reports volume  12 , Article number:  20485 ( 2022 ) Cite this article

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Despite widespread public interest in problematic gaming interventions, questions regarding the empirical status of treatment efficacy persist. We conducted pairwise and network meta-analyses based on 17 psychological intervention studies on excessive gaming ( n  = 745 participants). The pairwise meta-analysis showed that psychological interventions reduce excessive gaming more than the inactive control (standardized mean difference [SMD] = 1.70, 95% confidence interval [CI] 1.27 to 2.12) and active control (SMD = 0.88, 95% CI 0.21 to 1.56). The network meta-analysis showed that a combined treatment of Cognitive Behavioral Therapy (CBT) and Mindfulness was the most effective intervention in reducing excessive gaming, followed by a combined CBT and Family intervention, Mindfulness, and then CBT as a standalone treatment. Due to the limited number of included studies and resulting identified methodological concerns, the current results should be interpreted as preliminary to help support future research focused on excessive gaming interventions. Recommendations for improving the methodological rigor are also discussed.

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Introduction

Excessive gaming refers to an inability to control one’s gaming habits due to a significant immersion in games. Such an immersion may result in experienced difficulties in one’s daily life 1 , including health problems 2 , poor academic or job performance 3 , 4 , and poor social relationships 5 . Although there is debate regarding whether excessive gaming is a mental disorder, the 11th revision of the International Classification of Diseases (ICD-11) included Gaming Disorder as a disorder in 2019 6 . While there is no formal diagnosis for Gaming Disorder listed in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), the DSM-5 included Internet Gaming Disorder (IGD) as a condition for further study 7 . In the time since the DSM-5’s publication, research on excessive gaming has widely continued. Although gaming disorder’s prevalence appears to be considerably heterogeneous by country, results from a systematic review of 53 studies conducted between 2009 and 2019 indicated a global prevalence of excessive gaming of 3.05% 8 . More specifically, a recent study found that Egypt had the highest IGD prevalence rate of 10.9%, followed by Saudi Arabia (8.8%), Indonesia (6.1%), and India (3.8%) among medical students 9 .

While the demand for treatment of excessive gaming has increased in several countries 10 , standard treatment guidelines for problematic gaming are still lacking. For example, a survey in Australia and New Zealand revealed that psychiatrics— particularly child psychiatrists, reported greater frequency of excessive gaming in their practice, yet 43% of the 289 surveyed psychiatrists reported that they were not well informed of treatment modalities for managing excessive gaming 11 . Similarly, 87% of mental health professionals working in addiction-related institutions in Switzerland reported a significant need for professional training in excessive gaming interventions 12 . However, established services for the treatment of gaming remain scarce and disjointed.

Literature has identified a variety of treatments for excessive gaming, but no meta-analysis has yet been conducted on effectiveness of the indicated interventions. The only meta-analysis to date has focused on CBT 13 , and while results demonstrated excellent efficacy in reducing excessive gaming. However, the study did not compare the intervention with other treatment options. Given that gaming behavior is commonly affected by cognitive and behavioral factors as well as social and familial factors 14 , 15 , 16 , it would also be important to examine the effectiveness of treatment approaches that reflect social and familial influences. While two systematic reviews examined diverse therapeutic approaches, they primarily reported methodological concerns of the current literature and did not assess the weight of evidence 17 , 18 . Given that studies in this area are rapidly evolving and studies employing rigorous methodological approaches have since emerged 19 , 20 , a meta-analytic study that analyzes and synthesizes the current stage of methodological limitations while also providing a comprehensive comparison of intervention options is warranted.

In conducting such a study, undertaking a traditional pairwise meta-analysis is vital to assess overall effectiveness of diverse interventions. Particularly, moderator and subgroup analyses in pairwise meta-analysis provide necessary information as to whether effect sizes vary as a function of study characteristics. Furthermore, to obtain a better understanding of the superiority and inferiority of all clinical trials in excessive gaming psychological interventions, it is useful to employ a network meta-analysis, which allows for a ranking and hierarchy of the included interventions. While a traditional pair-wise analysis synthesizes direct evidence of one intervention compared with one control condition, a network meta-analysis incorporates multiple comparisons in one analysis regardless of whether the original studies used them as control groups. It enters all treatment and control arms of each study, and makes estimates of the differences in interventions by using direct evidence (e.g., direct estimates where two interventions were compared) and indirect evidence (e.g., generated comparisons between interventions from evidence loops in a network 21 . Recent meta-analytic studies on treatments for other health concerns and disorders have used this analysis to optimize all available evidence and build treatment hierarchies 22 , 23 , 24 .

In this study, the authors used a traditional pairwise meta-analysis and network meta-analysis to clarify the overall and relative effectiveness of psychological treatments for excessive gaming. The authors also conducted a moderator analysis to examine potential differences in treatment efficacy between Randomized Controlled Trials (RCTs) and non-RCTs, age groups, regions, and research qualities. Finally, the authors examined follow-up treatment efficacy and treatment effectiveness on common comorbid symptoms and characteristics (e.g., depression, anxiety, and impulsivity).

The protocol for this review has been registered in the International Prospective Register of Systematic Review (PROSPERO 2021: CRD 42021231205) and is available for review via the following link: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=231205 . Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) network meta-analysis checklist 25 is included in Supplementary Material 1 .

Identification and selection of studies

The authors searched seven databases, which included ProQuest, PubMed, Scopus, Web of Science, PsycINFO, Research Information Sharing Service (RISS), and DBpia. Given that a substantial number of studies have been published particularly in East Asia and exclusion of literature from the area in languages other than English has been discussed as a major limitation in previous reviews 17 , 18 , the authors gave special attention to gaming treatment studies in English and other languages from that geographical area. Additionally, the authors searched Google Scholar to ensure that no studies were accidentally excluded. The authors conducted extensive searches for studies published in peer-reviewed journals between the first available year (year 2002) and October 31, 2022, using the following search terms: “internet”, or “video”, or “online”, or “computer”, and “game”, or “games”, or “gaming”, and “addiction”, or “addictions”, or “disorder”, “disorders”, or “problem”, or “problems”, or “problematic”, or “disease”, or “diseases”, or “excessive”, or “pathological”, or “addicted”, and “treatment”, or “treatments”, or “intervention”, or “interventions”, or “efficacy”, or “effectiveness”, or “effective”, or “clinical”, or “therapy”, or “therapies”. Search strategies applied to each database is provided in Supplementary Material 2 .

The authors included studies that recruited individuals who were excessively engaging in gaming, according to cutoff scores for different game addiction scales. Since there is not yet an existing consensus on operational definitions for excessive gaming, the authors included studies that recruited individuals who met high-risk cutoff score according to the scales used in each respective study (e.g., Internet Addiction Test [modified in game environments] > 70). The authors also sought studies that provided pretest and posttest scores from the game addiction scales in both the intervention and control groups. Studies meeting the following criteria were excluded: (a) the study targeted excessive Internet use but did not exactly target excessive gaming; (b) the study provided a prevention program rather than an intervention program; (c) the study provided insufficient data to perform an analysis of the effect sizes and follow-up contact to the authors of such studies did not yield the information necessary for inclusion within this paper; and (d) the study conducted undefinable types of intervention with unclear psychological orientations (e.g., art therapy with an undefined psychological intervention, fitness programs, etc.).

Two authors (D.L. and S.L.) independently screened the titles and abstracts of articles identified by the electronic searches and excluded irrelevant studies. A content expert (J.K.) examined the intervention descriptions to determine intervention types that were eligible for this review. All treatments were primarily classified based on the treatment theory, protocol, and descriptions about the procedures presented in each paper. D.L. and S.L.—both of whom have been in clinical training for 2 years categorized treatment type, to which J.K., a licensed psychologist, cross-checked and confirmed the categorization. The authors resolved disagreements through discussion. The specific example of intervention type classification is provided in Supplementary Material 3 .

Risk of bias and data extraction

Three independent authors assessed the following risks of bias among the included studies. The authors used the Risk of Bias 2.0 (RoB 2) tool for RCT studies and the Risk Of Bias In Non-Randomized Studies of Intervention (ROBINS-I) tool for non-RCT studies. The RoB 2 evaluates biases of (a) randomization processes; (b) deviations from intended interventions; (c) missing outcome data; (d) measurement of the outcome; and (e) selection of the reported result, and it categorizes the risk of bias in each dimension into three levels (low risk, moderate risk, and high risk). The ROBINS-I evaluates biases of (a) confounding variables; (b) selection of participants; (c) classification of interventions; (d) deviations from intended interventions; (e) missing data; (f) measurement of outcomes; and (g) selection of the reported result, and it categorizes the risk of bias in each dimension into five levels (low risk, moderate risk, serious risk, critical risk, and no information). After two authors (D.L. and S.L.) assessed each study, another author (J.K.) cross-checked the assessment.

For each study, the authors collected descriptive data, which included the sample size as well as participants’ ages, and regions where the studies were conducted. The authors also collected clinical data, including whether the study design was a RCT, types of treatment and control, treatment duration, and the number of treatment sessions. Finally, the authors collected data on the follow-up periods and the measurement tools used in each study.

Data analysis

The authors employed separate pairwise meta-analyses in active control and inactive control studies using R-package “meta” 26 and employed a random-effects model due to expected heterogeneity among studies. A random-effects model assumes that included studies comprise random samples from the larger population and attempt to generalize findings 27 . The authors categorized inactive control groups including no treatment and wait-list control and categorized active control groups including pseudo training (e.g., a classic stimulus-control compatibility training) and other types of psychological interventions (e.g., Behavioral Therapy, CBT, etc.). The authors also used the bias-corrected standardized mean change score (Hedges’ g ) due to small sample sizes with the corresponding 95% confidence interval 28 . The authors’ primary effectiveness outcome was a mean score change on game addiction scales from pre-treatment to post-treatment. Hedges’ g effect sizes were interpreted as small ( g  = 0.15), medium ( g  = 0.40) and large ( g  = 0.75), as suggested by Cohen 29 . The authors used a conservative estimate of r  = 0.70 for the correlation between pre-and post-treatment measures 30 , and to test heterogeneity, the authors calculated Higgins’ I 2 , which is the percentage of variability in effect estimates due to heterogeneity among studies rather than chance. I 2  > 75% is considered substantial heterogeneity 31 .

The authors conducted moderator analyses as a function of RCT status (RCT versus non-RCT), age group (adolescents versus adults), region (Eastern versus Western), and research quality (high versus low). The authors divided high versus low quality studies using median values of research quality scores (RCT: low [0–2] versus high [3–5], non-RCT: low [0–4] versus high [5]). The authors calculated Cochran’s Q for heterogeneity: A significant Q value indicates a potentially important moderator variable. For the subgroup analyses of follow-up periods and other outcomes, the authors conducted separate pairwise analyses in 1- to 3-month follow-up studies and in 4- to 6-month follow-up studies and separate analyses in depression, anxiety, and impulsivity outcome studies.

The authors sought to further explore relative effectiveness of treatment types and performed a frequentist network meta-analysis using the R-package “netmeta” 4.0.4 version 26 . To examine whether transitivity and consistency assumptions for network meta-analysis were met, the authors assessed global and local inconsistency. To test network heterogeneity, the authors calculated Cochran’s Q to compare the effect of a single study with the pooled effect of the entire study. The authors drew the geometry plot of the network meta-analysis through the netgraph function in “netmeta”, and the thicker lines between the treatments indicated a greater number of studies.

The authors presented the treatment rankings based on estimates using the surface area under the cumulative ranking curve (SUCRA) 32 . The SUCRA ranged from 0 to 100%, with higher scores indicating greater probability of more optimal treatment. The authors also generated a league table to present relative effectiveness between all possible comparisons between treatments. When weighted mean difference for pairwise comparisons is bigger than 0, it favors the column-defining treatment. Finally, funnel plots and Egger’s test were used to examine publication bias.

Included studies and their characteristics

Figure  1 presents the flow diagram of the study selection process. The authors identified 1471 abstracts in electronic searches and identified an additional seven abstracts through secondary/manual searches (total n  = 1478). After excluding duplicates ( n  = 765) and studies that did not meet the inclusion criteria based on the study abstract ( n  = 550), the authors retrieved studies with potential to meet the inclusion criteria for full review ( n  = 163). Of these, 144 studies were excluded due to not meeting inclusion criteria based on full-text articles, leaving 19 remaining studies. Of the 19, two studies did meet this paper’s inclusion criteria but were excluded from this network meta-analysis 33 , 34 because the consistency assumption between direct and indirect estimates was not met at the time of this study's consideration based on previous studies 35 , 36 . Therefore, a total of 17 studies were included in this network meta-analysis, covering a total of 745 participants 36 .

Flow diagram of the study selection process.

Table 1 lists the characteristics of the 17 included studies. CBT ( n  = 4), Behavioral Treatment (BT) + Mindfulness ( n  = 4), and BT only ( n  = 4) were most frequently studied, followed by CBT + Family Intervention ( n  = 1), CBT + Mindfulness ( n  = 1), virtual reality BT ( n  = 1), Mindfulness ( n  = 1), and Motivational Interviewing (MI) + BT ( n  = 1). Seven studies were conducted in Korea and six were conducted in China, followed by Germany and Austria ( n  = 1), Spain ( n  = 1), the United States ( n  = 1), and the Philippines ( n  = 1). Twelve articles were written in English, and five articles were written in a language other than English. Nine studies conducted a follow-up assessment with periods ranging from one to three months, and two studies conducted a follow-up assessment with periods ranging four to six months. In one study 20 , the authors described their 6-month follow-up but did not present their outcome value, and thus only two studies were included in the four- to six-month follow-up analysis. Among the 17 included studies, eight had no treatment control group, five had an active control group (e.g., pseudo training, BT, and CBT), and four had a wait-list control group. Seven of the studies were RCT studies, and 10 were non-RCT studies.

Pairwise meta-analysis

The results of meta-analyses showed a large effect of all psychological treatments when compared to any type of comparison groups ( n  = 17, g  = 1.47, 95% CI [1.07, 1.86]). The treatment effects were separately provided according to active versus inactive comparison groups in Fig.  2 . The effects of psychological treatments were large when compared to the active control ( n  = 5, g  = 0.88, 95% CI [0.21, 1.56]) or inactive control ( n  = 12, g  = 1.70, 95% CI: [1.27, 2.12]). Substantial heterogeneity was evident in studies that were compared to both the active controls (I 2  = 72%, < 0.01) and inactive controls at p -value level of 0.05 (I 2  = 69%, p  < 0.001).

Pairwise Meta-analysis. Psychological treatment effects on excessive gaming by comparison group type (active and inactive controls). SMD standardized mean difference, SD standard deviation,  CI confidence interval, I 2  = Higgins' I 2 .

Moderator analysis

As shown in Table 2 , the moderator analysis suggested that effect sizes were larger in non-RCT studies ( n  = 10, g  = 1.60, 95% CI [1.36, 1.84]) than RCT studies ( n  = 7, g  = 1.26, 95% CI [0.30, 2.23]). However, the results of a Q-test for heterogeneity yielded insignificant results (Q = 0.44, df[Q] = 1, p  = 0.51), indicating that no statistically significant difference in treatment efficacy at p level of 0.05 between RCT and non-RCT studies.

The results of Q-test for heterogeneity did not yield any significant results, indicating no significant differences in treatment efficacy between adults and adolescents (Q = 2.39, df[Q] = 1, p  = 0.12), Western and Eastern regions (Q = 0.40, df[Q] = 1, p  = 0.53), or low and high research qualities among RCT studies (Q = 2.25, df[Q] = 1, p  = 0.13) and non-RCT studies (Q = 3.06, df[Q] = 1, p  = 0.08).

Subgroup analysis

The results demonstrated that the treatment effect was Hedges’ g  = 1.54 (95% CI [0.87, 2.21]) at 1-to-3-month follow-up and Hedges’ g  = 1.23 (95% CI [0.77, 1.68]) 4- to-6-month follow-up. The results also showed that the treatment for excessive gaming was also effective on depression and anxiety. Specifically, treatment on depression was Hedges’ g  = 0.52 (95% CI: [0.22, 0.81], p  < 0.001), and anxiety was Hedges’ g  = 0.60 (95% CI [0.11, 1.08], p  = 0.02), which are medium and significant effects. However, the effect on impulsivity was insignificant, Hedges’ g  = 0.26 (95% CI [− 0.14, 0.67], p  = 0.20).

Network meta-analysis

As shown in Fig.  3 , a network plot represents a connected network of eight intervention types (CBT, BT + Mindfulness, BT, Virtual Reality BT, CBT + Mindfulness, CBT + Family, MI + BT, and Mindfulness) and three control group types (wait-list control, no treatment, treatment as usual). The widest width of nodes was observed when comparing BT + Mindfulness and no treatment, indicating that those two modules were most frequently compared. No evidence of global inconsistency based on a random effects design-by-treatment interaction model was found (Q = 8.5, df[Q] = 7, p  = 0.29). Further, local tests of loop-specific inconsistency did not demonstrate inconsistency, indicating that the results from the direct and indirect estimates were largely in agreement ( p  = 0.12- 0.78).

Network plot for excessive gaming interventions. Width of lines and size of circles are proportional to the number of studies in each comparison. BT behavioral therapy, CBT cognitive behavioral therapy, Family family intervention, MI motivational interviewing, TAU treatment as usual.

As shown in Fig.  4 , according to SUCRA, a combined intervention of CBT and Mindfulness ranked as the most optimal treatment (SUCRA = 97.1%) and demonstrated the largest probability of effectiveness when compared to and averaged over all competing treatments. A combined treatment of CBT and Family intervention ranked second (SUCRA = 90.2%), and Mindfulness intervention ranked third (SUCRA = 82.1%). As shown in Table 3 , according to league table, CBT + Mindfulness intervention showed positive weighted mean difference values in the lower diagonal, indicating greater effectiveness over all other interventions. The CBT + Mindfulness intervention was more effective than CBT + Family or Mindfulness interventions, but their differences were not significant (weighted mean differences = 0.23–1.11, 95% CI [− 1.39 to 2.68]). The top three ranked interventions (e.g., CBT + Mindfulness, CBT + Family intervention, and Mindfulness in a row) were statistically significantly superior to CBT as a standalone treatment as well as the rest of treatments.

Surface under the cumulative ranking curve (SUCRA) rankogram of excessive gaming. BT behavioral therapy, CBT cognitive behavioral therapy, Family family intervention, MI motivational interviewing, TAU treatment as usual.

Risk of bias

Figure  5 displays an overview of the risk of bias across all included studies. Of note was that in the RCT studies, bias due to missing outcome data was least problematic, indicating a low dropout rate (six out of seven studies). In contrast, bias due to deviations from intended interventions was most problematic, indicating that, in some studies, participants and trial personnel were not blinded and/or there was no information provided as to whether treatments adhered to intervention protocols (six out of seven studies). In the non-RCT studies, bias in the selection of participants in the study was least problematic, indicating that researchers did not select participants based on participant characteristics after the start of intervention (10 out of 10 studies). In contrast, bias in the measurement of outcomes was most problematic, indicating that participants and outcome assessors were not blinded and/or studies used self-reported measures without clinical interviews (10 out of 10 studies).

Overview of risk of bias results across all included studies. Cl bias in classification of interventions, Co bias due to confounding, De bias due to deviations from intended interventions, Me bias in measurement of the outcome, Mi bias due to missing outcome data, R bias arising from the randomization process, RoB risk of bias, ROBINS-I risk of bias in non-randomized studies of intervention, Sp bias in selection of participants in the study, Sr bias in selection of the reported result.

Funnel plots and Egger’s test showed no evidence of publication in network meta-analyses. Funnel plots were reasonably symmetric and the result from Egger’s test for sample bias were not significant ( p  = 0.22; see Supplementary Material 4 ).

In this pairwise and network meta-analyses, the authors assessed data from 17 trials and analyzed the overall and relative effectiveness of eight types of psychological treatments for reducing excessive gaming. The pairwise meta-analysis results indicated large overall effectiveness of psychological treatments in reducing excessive gaming. Although the effectiveness was smaller when compared to the active controls than when compared to the inactive controls, both effect sizes were still large. However, this result needs to be interpreted with caution because there are only seven existing RCT studies and several existing low-quality studies. Network meta-analysis results indicated that a combined treatment of CBT and Mindfulness was the most effective, followed by a combined therapy of CBT and Family intervention, Mindfulness, and then CBT as a standalone treatment, however, this finding was based on a limited number of studies. Overall, the findings suggest that psychological treatments for excessive gaming is promising, but replications are warranted, with additional attention being placed on addressing methodological concerns.

The large effect of psychological treatments in reducing excessive gaming seems encouraging but the stability and robustness of the results need to be confirmed. These authors’ moderator analysis indicated that the effect size of non-RCT studies was not significantly different from that of RCT studies. The authors conducted a moderator analysis using the research quality score (high vs low) and found that research quality did not moderate the treatment effect. The authors also examined publication bias using both funnel plots and Egger’s test and found no evidence of publication bias in network meta-analysis. Because most of the studies included in the review were from Asian countries, the authors examined the generalizability of the finding by testing moderator analysis by regions and found no significant difference of treatment effect sizes between Eastern and Western countries. Finally, although limited studies exist, treatment benefits did not greatly diminish after 1–6 months of follow-ups, indicating possible lasting effects.

Network meta-analysis findings provide some preliminary support for the notion that a combined treatment of CBT and Mindfulness and a combined treatment of CBT and Family intervention are most effective in addressing individuals’ gaming behaviors. These combined therapies were significantly more effective than the CBT standalone approach. CBT has been studied and found to be highly effective in addiction treatment—particularly in reducing excessive gaming due to its attention to stimulus control and cognitive restructuring 13 . However, adding Mindfulness and family intervention may have been more effective than CBT alone, given that gaming is affected not only by individual characteristics, but also external stress or family factors.

Mindfulness generally focuses on helping individuals to cope with negative affective states through mindful reappraisal and aims to reduce stress through mindful relaxation training. The effectiveness of Mindfulness has been validated in other substance and behavioral addiction studies such as alcohol 37 , gambling 38 , and Internet 39 addiction treatments. Indulging in excessive gaming is often associated with the motivation to escape from a stressful reality 40 , and mindful exercises are likely to help gamers not depend on gaming as a coping strategy.

Because excessive gaming is often entangled with family environments or parenting-related concerns—particularly with adolescents, addressing appropriate parent–adolescent communication and parenting styles within excessive gaming interventions are likely to increase treatment efficacy 41 , 42 , 43 . Based on a qualitative study focused on interviews with excessive gamers 43 , and per reports from interviewed gamers, parental guidance to support regulatory control and encouragement to participate in other activities are important factors to reduce excessive gaming. However, at the same time, if parents excessively restrict their children’s behavior, children will feel increased stress and may further escape into the online world through gaming 44 as a means of coping with their stress. Our study indicates that appropriate communication among parents and adolescents in addition to parenting styles with respect to game control must be discussed in treatment. However, because only two studies examined the top two ranked combined interventions within this paper, such findings warrant replication.

Limitations and future directions

These authors identified methodological limitations and future directions in the reviewed studies, which include the following. The authors included non-RCTs to capture data on emerging treatments, but a lack of RCT studies contributes to this paper’s identified methodological concerns. Of 17 studies included, seven were RCT studies and 10 were non-RCT studies. The lack of RCT studies has been repeatedly mentioned in previous review studies 17 , 18 . In fact, one of the two identified reviews 17 made the criticism that even CBT (the most widely studied treatment for excessive gaming) was mostly conducted in non-RCT studies, which was commensurate with this paper’s data (only one out of four CBT studies included in this review is a RCT). Including non-RCTs may be likely to increase selection bias by employing easily accessible samples and assigning participants with more willingness (which is an indicator of better treatment outcome) to intervention groups. Selection bias may have increased the effect size of treatments than what is represented in reality and may limit the generalizability of this finding. Thus, more rigorous evaluation through RCTs is necessary in future studies.

While there are concerns surrounding assessment tools, given that all included studies used self-report measures without clinical interviews, this may lead to inaccurate results due to perceived stigma. Additionally, 11 self-reported measurement tools were employed in the included studies—and some of those tools may have poor sensitivity or specificity. A previous narrative review 45 and a recent meta-analytic review 46 suggested that the Game Addiction Scale-7, Assessment of Internet and Computer Addiction Scale-Gaming, Lemmens Internet Gaming Disorder Scale-9, Internet Gaming Disorder Scale 9- Short Form, and Internet Gaming Disorder Test-10 have good internal consistency and test–retest reliability. Thus, there is a need for studies to employ clinical interviews and self-report measures with good psychometric features.

Many studies in this included review did not describe whether participants and experimenters were blinded and there was no information about whether treatments adhered to intervention protocols. Although blinding of participants and personnel may be impossible in most psychotherapy studies, it is crucial to evaluate possible performance biases such as social desirability. Also, a fidelity check by content experts is needed to confirm whether treatments adhered to intervention protocols.

Finally, future studies need to examine treatment efficacy in treating both excessive gaming and its comorbid psychiatric symptoms. Internet/gaming addiction has been reported to have a high comorbidity with attention deficit hyperactivity disorder, depression, anxiety, and other substance abuse 47 , 48 . Our results showed that CBT, BT, and BT + Mindfulness may be effective in reducing depression or anxiety symptoms of excessive gamers. However, other psychological and/or pharmacological treatments such as CBT + Bupropion or Bupropion as a standalone treatment have been also reported as potentially effective treatments for excessive gamers with major depressive disorder 49 , 50 . Thus, it would be worthwile to examine efficacy of treatments on excessive gamers with dual diagnoses.

TO the best of the authors’ knowledge, this is the first pairwise meta-analytic and network meta-analytic study that examined the overall effectiveness of psychological treatments and compared the relative effectiveness of diverse treatment options for excessive gaming. Although the authors intentionally used network meta-analysis because of its usefulness in comparing relative effectiveness of currently existing literature, this finding should be interpreted with caution due to the small number of studies. However, as previously indicated, the global prevalence of excessive gaming highlights the need for greater attention to this topic. Studies focused on the effectiveness of diverse gaming interventions help meet the call for further inquiry and study on this topic placed by the DSM-5 7 , and allow greater advances to be made in treating individuals who may have difficulty controlling excessive gaming habits. As such, this study can provide preliminary support for beneficial treatment interventions for excessive gaming as well as recommendations for more rigorous studies to be directed at helping those who have excessive gaming habits.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

•Indicates studies used in the meta-analysis

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This research was supported by the project investigating scientific evidence for registering gaming disorder on Korean Standard Classification of Disease and Cause of Death funded by the Ministry of Health and Welfare and Korea Creative Content Agency.

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J.K., K.-H.C., J.C., S.-H.S., and W.-Y.A. contributed to the conception and design of the study. J.K. wrote the draft of the manuscript and D.B. reviewed and edited the draft. D.L., S.L., and S.S. extracted the data and performed the analyses.

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Kim, J., Lee, S., Lee, D. et al. Psychological treatments for excessive gaming: a systematic review and meta-analysis. Sci Rep 12 , 20485 (2022). https://doi.org/10.1038/s41598-022-24523-9

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Computer Gaming and Physiological Changes in the Brain: An Insight from QEEG Complexity Analysis

Zahrasadat hosseini.

1 Institute for Cognitive Science Studies, Tehran, Iran

Roya Delpazirian

Hossein lanjanian.

2 Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Mona Salarifar

4 Department of Management and Marketing, Semnan Branch, Islamic Azad University, Semnan, Iran

Peyman Hassani-Abharian

3 Institute for Cognitive Science Studies, Brain and Cognition Clinic, Tehran, Iran

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To compare the pattern of brain waves in video game addicts and normal individuals, a case–control study was carried out on both. Thirty participants were recruited from 14 to 20 years old males from two gaming centers. Twenty healthy participants were gathered from different schools in Tehran using the available sampling method. The QEEG data collection was performed in three states: closed-eye and open-eye states, and during a working memory task. As expected, the power ratios did not show a significant difference between the two groups. Regarding our interest in the complexity of signals, we used the Higuchi algorithm as the feature extractor to provide the input materials for the multilayer perceptron classifier. The results showed that the model had at least a 95% precision rate in classifying the addicts and healthy controls in all three types of tasks. Moreover, significant differences in the Higuchi Fractal Dimension of a few EEG channels have been observed. This study confirms the importance of brain wave complexity in QEEG data analysis and assesses the correlation between EEG-complexity and gaming disorder. Moreover, feature extraction by Higuchi algorithm can render support vector machine classification of the brain waves of addicts and healthy controls more accurate.

Introduction

During the last three decades, video games have become one of the major pastimes and one of the most growing industries worldwide (Sepehr & Head, 2012 ). 67% of all Americans play video games, for example. This is a highly varied phenomenon, ranging from primary games to highly advanced 3D ones, and is an increasingly frequent activity worldwide. Today, computer games include sophisticated virtual worlds, online competitions, and multiplayer strategic games. In 2008, the American National Purchase Diary (NPD) group reported that 3% of the 174 million players using PC, MAC, or game consoles were extreme gamers who are playing an average of 45 h a week. NPD reported that the percentage of extreme gamers had increased to 4% by 2010 ( https://www.npd.com/ ).

The internet gaming disorder (IGD) is a growing and prolonged behavioral pattern of gaming leading to behavioral and cognitive syndromes. It is the increasing loss of control over gaming, tolerance, and the presence of withdrawal syndrome. The addicts are usually 12 to 20 years old and spend 8–10 h a day or more playing video games. Preventing them from playing can lead to tension and anger and they may spend long stretches of time playing without food and sleep (American Psychiatric Association, 2013 ). According to the reports given by scientists and psychologists, gamers are increasing their playing time to a problematic degree. Investigations have also shown that, in some cases, gaming may seriously damage subjects' school, work, and social relationships in the real world.

Even though video gaming has some positive effects (including good concentration, memory, and solving skills), researchers have shown that excessive use of computer games may lead to negative effects like stress, aggressive behavior, verbal memory deficiency, depression, lowered cognitive abilities, sleeping disorders, anxiety and, behavioral addiction. Moreover, clinical evidence has shown that subjects addicted to online games experience biopsychological symptoms and complications. These symptoms may include the traditional symptoms of drug addiction such as hangover, changes in mood, adaptability, withdrawal, conflict, and recurrence symptoms. On the other hand, Given the increasing advancement of technology and, subsequently, computer games, the prevalence of this abnormality in societies can be expected. Thus, there is a growing need for further investigation of this phenomenon and its potential effects on people.

The first study about video gaming addiction was done by Ross, Finestone, and Lavin, who referred to it as "space invader obsession"(Ross et al., 1982 ), 10 years after the first video game release. Soper and Miller are pioneers in calling this disorder a kind of addictive behavior (Soper & Miller, 1983 ). The first assessment and questionnaire was conducted by Kimberly Young (Young, 1998 ). In 2013 after numerous studies with a variety of methods, DSM5 defined internet gaming disorder as addictive behavior. According to the Diagnostic and Statistical Manual of Psychiatric Disorders (DSM-5), computer-based or online gaming addiction is a preoccupation with playing games. It usually includes a group of players, often resulting in several consequences within 5 to 12 weeks (American Psychiatric Association, 2013 ).

Working memory plays an important role in facing addiction; thus it is unfavorably affected by addiction. Therefore, studying the consequences of gaming addiction on working memory is one of the interesting research subjects; however, it needs careful task designing. In task designing, it is important to just peruse a function by controlling other functions as much as possible (Hollnagel, 2003 ). Thus, a mental calculation task is considered a cognitive task. Related to working memory, a mental calculation is a routine, everyday ability that everyone possesses, regardless of their level of education. Moreover, focusing on the working memory, we tried to limit brain activity in other areas like auditory and visionary activities. Consequently, we assessed and compared the working memory of two groups via a mental calculation task.

Neurobiological differences have been discovered between healthy controls and individuals with Internet Gaming Disorder using PET, VBM, fMRI, rsfMRI, and EEG studies. As a popular technique in the assessment of the neurobiological differences, EEG is used to compare (i) excessive and addictive gaming, (ii) gaming addiction and other comorbid disorders, and (iii) gaming addiction (miscellaneous).

Brain dynamics are highly complicated, having multiple spatial–temporal scales. Thus, non-linear dynamics methods have been used to analyze the brain’s neurophysiological outputs. Stochasticity, determinism, causation, and correlations of neurodynamics are epitomized and quantified on the complexity measures of brain waves and other biosignals data of the brain. Causality, correlation, and stochastic phenomena turn QEEG data into a complex time series. There are several lines of evidence suggesting that this aspect of the brain waves is influenced by cognitive disorders. Thus, various complexity measures have been put forward to analyze EEG data, including measures developed using random fractal theory, information theory, and chaos theory.

Gao et al. Have described the different approaches to analyzing the complex dynamics. They distinguish between chaos and random phenomena. Chaos theory considers irregular behaviors in a complex system. These behaviors are deterministic and are caused by only a few degrees of freedom. Here, noise or intrinsic randomness is not of primary concern. In contrast, randomness is the pivotal principle of random fractal theory and information theory. They consider the dynamics of the system to be inherently random (Gao et al., 2007 ). While long-range correlations form the subject of random fractal theory, short-range correlations are considered in information theory. in their study on the relations among different complexity measures for EEG, Gao et al. “found that the variations of these complexity measures with time are either similar or reciprocal.” (Gao et al., 2011 ). In what follows, the salient steps of our study will be presented.

The Higuchi Algorithm Feature Extraction and Partition Membership Feature Selection Method

Feature extraction is the process of transforming original data to remove redundant or irrelevant information and producing a much smaller and more manageable data set of more discriminator variables. The goal of feature selection, however, is to reduce overfitting, improve accuracy, and decrease training time. After feature extraction and feature selection, the resulting features are valuable if they are highly correlated with the class and uncorrelated with each other. Hence, we are interested in the feature subset containing the minimum number of features that contribute to accuracy the most.

Fractal theory can be used to extract features from a series. The Higuchi Fractal Dimension (HFD) algorithm is a method for measuring the fractal dimension of discrete-time sequences (Higuchi, 1988 ; Kesić & Spasić, 2016 ). It is a criterion of the complexity and self-similarity of the signal. For time series x[1], x[2],..., x[n] , the Higuchi fractal can be calculated as:

The original series is converted to k  new series via Higuchi's algorithm; thus, from  [x 1 , x 2 , …, x N ],  where N is the length of the original time series, the following series are obtained:

where m is initial time; k is the time interval in the way that m  =  1, 2, …, k. L(m , k) is the length of each time series:

(N-1)/{int[(N-m)/k]k is a normalization factor and the average length is computed as

This procedure is repeated  k max  times for each  k  from 1 to  k max . This results in an array of mean values L(k). Then, a least-square method is used to find the slope of the line that best fits the curve of  ln(L(k))  versus  ln(1/k) . That slope is the Higuchi Fractal Dimension. HFD is a scalar feature. It is noteworthy that the selection of k max could strongly influence the results.

Fuzzy set theory was first introduced by Zadeh ( 1965 ). This theory renders classification flexible by allowing partial set membership rather than binary, 0 or 1, membership. The fuzzy function maps the membership degree of an element for a given set to a real value in between [0,1]. There are some feature selection methods based on the Partition generator function that comes from the fuzzy set idea.

This study is aimed at finding a simple approach with a low computational cost approach for discovering associations between clinical issues and modification of complexity of the neuronal activity. We expected to find the brain waves of healthy individuals to differ from gaming addicts. Therefore, the chaotic behavior of brain electrical activity was investigated through a time-domain analysis and extracting the fractal-like features of EEG signals. In line with our previous studies and to keep the approach simple and decrease the computation steps, we calculated the HFD for the total EEG signals (without focusing on any specific frequency band) was calculated (Roozbehi et al., 2020 ). Finally, the EEG signals obtained were classified based on these fractal-like features. We believe our research is more valuable as the COVID-19 lockdown has caused people to be at a higher risk of gaming addiction disorders.

Materials and Methods

Methodology and research design.

This experimental case–control study was carried out on video game addicts and normal individuals living in Tehran. Thirty participants were recruited from 14 to 20 years old boys from two gaming centers using available sampling. To exclude individuals with any learning or mental disabilities, the experimental group was screened and all participants were interviewed by a qualified psychologist. Finally, 3 subjects were excluded due to their Symptom Checklist-90-Revised (SCL-90-R) scores while 7 others were excluded based on their Video game addiction test (VAT) scores. 20 participants were selected as addicted gamers. Twenty subjects were randomly selected from normal students aged 14 to 20. Eventually, two groups of 20 participants (addicted and normal) were formed. Ethical authorization for research was received through letter No. IR.TUMS.VCR.REC.1395.1562 from the Ethics Committee of the Vice-Chancellor of Research of Tehran University of Medical Sciences on January 29, 2017. The inclusion criteria for the study included males 14 to 20 years of age and the exclusion criteria included an SCL-90-R score higher than 1 and a VAT score less than 2.5. The following questionnaires were used as research tools:

The Symptom Checklist-90-Revised (SCL-90-R)

This questionnaire is a tool for the rapid assessment of subjects’ mental pathology. It is a 90-item questionnaire assessing 9 psychological dimensions including physical complaints, obsession, and coercion, interpersonal sensitivity, depression, anxiety, aggression, phobic anxiety, paranoia, and psychosis. The validity and reliability of the Persian translation of this test were confirmed in Iran by Bagheriyazdi et al. ( 1994 ) and its re-test reliability was ascertained at 0.97 within a week's interval. This test was taken by all participants via a trained psychologist in a clinical interview. Those scoring higher than 0.77 in each item were excluded from the test. The anxiety subscale was of special interest in exclusion as it may affect arousal activity and the brain’s electrical activity. The mean ANX score was 0.44 (± 0.24) for the control group, while the addicted group scored 0.48 (± 0.22)—a seemingly insignificant difference.

Video Game Addiction Test (VAT)

There are several instruments to assess gaming addiction. Among the questionnaires designed to detect Internet and gaming addiction, the The Compulsive Internet Use Scale (CIUS) internet abuse questionnaire developed by Meerkerk et al., Young’s Internet Addiction Test, and Van Rooij’s video game addiction test seemed most suited for use in this study (Meerkerk et al., 2009 ; Van Rooij & Prause, 2014 ; Van Rooij et al., 2011 ). However, the Van Rooij video game addiction test was the one finally employed since DSM-5 has introduced criteria to detect Internet addiction and Internet gaming: brain drain, withdrawal complications, compatibility (more time spent playing games), lack of control, loss of other interests, using despite negative consequences, temptation, changes in mood, loss of a job, relationship, or other important aspects of life and compatibility was not considered in the first two questionnaires (American Psychiatric Association, 2013 ). The validity and reliability of the translated version of this questionnaire were investigated in an article entitled "Psychometric Properties of the Persian Translation of the Video Gaming Addiction Test (VAT)” (Hosseini et al., 2019 ).

The subjects in both groups underwent EEG testing in three steps, each lasting 3 min. The test was performed using the 21-channel EEG system (Medicom; Russia, sampling rate: 256 Hz) by qualified personnel and according to guidelines of recording location and situations. The electrodes used consisted of Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5, T6, FZ, CZ, PZ, and all electrodes amplitudes were evaluated relative to the earlobes (A1, A2) (Fig.  1 ). The recordings were taken between 9 and 13 o’clock, with each recording taking about 20 min, including preparation and cleanings. This experimental design included three parts; resting state, open eye state, and cognitive task. The working memory was selected for assessment, as the main target of the lesion in addiction. Based on prior studies, mental calculations and arithmetics are related to working memory. As such, the working memory function is one of the main components of cognitive ability. In accordance with the literature on working memory and mental calculation and to reach a pure activation of working memory, here the visuospatial working memory and verbal working memory omission subvocal task was preferred. To achieve working memory function in arithmetics, a complex calculation is more preferable to a simpler one, because in a simple calculation the solution is retrieved from long-term memory instead of working memory. Another important factor in designing tasks and choosing a routine arithmetic fact 1 was removing the effect of expert and non-expert strategies in problem-solving, which leads to the activation of different brain areas.

Electrode positions for the 19-channel EEG apparatus

In the first stage, each participant was asked to sit in a relaxed state and close his eyes. He was also asked to not think of anything and refrain from moving his body. In the second stage, he was asked to look at a point marked on the opposite wall without blinking and or moving. In the third stage, the participant was asked to act as he did in the second stage but to also count down by 3 s from 1000 mentally and as fast as possible.

Data Gathering

The final statistical population of this study comprised 40 subjects (20 addicts + 20 controls). All of the participants of the IGD group were between 14 and 20 years old, living in Tehran, and all right-handed (based on the Edinburgh Handedness Inventory). A clinical psychologist conducted all the diagnostic interviews for the primary assessments of participants.

As mentioned in the previous sections, DATA was recorded from the surface of the scalp of participants according to a standardized electrode placement scheme.

The next step was signal pre-processing. After that, data analysis was performed using a machine-learning approach. The machine learning perspective consists of five parts: (1) pre-processing; (2) feature extraction; (3) feature selection and dimensionality reduction; (4) classification; and (5) post-processing. In this study, we applied a machine learning approach and let the model filter the features. Therefore, the data obtained from all channels were used to extract and select the most relevant features.

The fractal dimensions were extracted using the HFD algorithm as the features which epitomize the obtained data. Moreover, feature selection was applied based on fuzzy set theory using the partition membership filtering technique and was used to select the subset of features. Supervised machine-learning classification using support vector machine (SVM) method was applied for feature selection. Waikato Environment for Knowledge Analysis (WEKA), a non-commercial and open-source data mining system was utilized for this purpose. To calculate these feature vectors for all instances, WEKA’s PartitionMembershipFilter was employed, which can apply any partition generator to a given dataset.

Calculation

Signal preprocessing was performed on the recordings obtained from all 19 channels for each participant according to the following steps via EEGLAB v.2019:

• Artefact Detection and removal: EEGLAB was used to remove the artifacts of the recorded EEG signals. To this aim, from the “Tools” menu “Remove baseline” and “Reject data using Clean Raw data and ASR” options were used to remove artifacts.
• To remove out-of-band noise, the EEG time series were filtered within a range of 1–60 Hz using a Finite Impulse Response (FIR) filter.
• EEG Re-referencing: In this study, a common average referencing was used, i.e. the average of all channels.
• Line noise suppression was performed using a notch filter at 50 Hz.
• Bad channels or missing channels were repaired by replacing them with the average of all neighbors (interpolation).
• Ran ICA to detect components of each signal in each channel.
• Removed the components originated from undesired sources like electrocardiography (ECG) and electromyography (EMG) to prepare artifact-free EEG signals.

During the preprocessing steps, some records were detected as being corrupted or disturbed; therefore, the final numbers of analyzed participants of different groups were as follows: Healthy and closed eye (Hec) = 20; Healthy and open eye (Heo) = 17; Healthy and calculation task (Hct) = 17; Addicted and closed eye (Aec) = 17; Addicted and open eye (Aeo) = 18; Addicted and calculation task (Act) = 18. After preprocessing, for each channel of each participant, a total of 44,700 records (equals 179 s) were kept. However, the HFD algorithms converted this series into two scalars. Therefore, we had 19*2 = 38 features for every participant.

The fractal dimensions were extracted via the HFD algorithm with k max  =  8 . These features were filtered and combined through WEKA’s Partition Membership method before using SVM and MLP as two salient methods of supervised machine learning algorithm and classification problems divide the participants into different classes. The results are displayed in Table ​ Table1 1 .

The obtained results from the classifiers

The independent t-test used to compare two groups in each channel revealed meaningful differences 2 channels: the O2 channel (p.value b  = 0.0339) in open-eye state and the F4 channel (p.value a  = 0.027, p.value a  = 0.006) in closed-eye state. Meaningful differences with two points level (0.05) found in another two channels: at P4 channel (p.value b  = 0.0529) in closed-eye state and F3 channel (p.value b  = 0.0524) in closed-eye state.

Finding an appropriate method to recognize individuals addicted to gaming with neuroimaging devices is an important matter that eases the process of diagnosing the disorder and facilitates the treatment process. EEG is relatively low-cost, easy to use, and portable, making it is a very good fit for everyday clinical needs. Thus, in the current study, the brain waves and working memory of IGD individuals and normal people were compared. To this aim, QEEG data were obtained in 3 different situations and the Higuchi complexity-measure and Partition Membership Filter were applied. Based on the analysis, it was possible to classify the participants with acceptable accuracy.

There are studies conducted to find a neurophysiological marker for IGD. Park et al. have proposed the heightened phasic synchrony in the gamma band during the resting state as a marker of IGD (Park et al., 2017 ). Son et al. suggest using lower absolute beta power as a potential trait marker for IGD (Son et al., 2015 ). In another study, Park et al. found significantly higher intrahemispheric fast-frequency coherence among IGD patients and proposed using it as a neurophysiological trait marker for these patients (Park et al., 2018 ). We tried to diagnose IGD based on the change in a complexity measure of whole QEEG data; as it is a more practical method and easier to use. Moreover, we find it a more reliable method as other suggested markers are not shown to be consistent.

When it comes to interpreting EEG data, researchers have a wide range of analytical tools at their disposal (Dauwels et al., 2010 ; Delorme & Makeig, 2004 ) and in recent years they have explored some new correlations between different measures of complexity (Cao & Slobounov, 2011 ; Dauwels et al., 2010 ; Sitt et al., 2014 ; Šušmáková & Krakovská, 2008 ; Weiss et al., 2011 ).

The fractal dimension (FD) of a signal is a measure for describing its complexity and self-similarity in the time domain. The fractal dimension of EEG was calculated using Higuchi’s algorithm, which displayed the best result for EEG and other electrophysiological data. The Higuchi algorithm was run with the maximal 10 scales and k max  = 8 parameters. The Higuchi complexity-measure and Partition Membership Filter have proven a good method for diagnosing IGD simply and more accurately. According to Table ​ Table1, 1 , it has been shown with 95% accuracy that our finding is a good result for differing individuals with IGD and normal people.

Several resting-state EEG studies related to behavioral addiction have also been conducted. Lee et al. used EEG data to compare individuals having gambling disorders with normal people, in resting-state and open-eye states (Lee et al., 2017 ). Son et al., used the same method to compare individuals with IGD, AUD, and healthy controls (Son et al., 2015 ). Lee et al . conducted an eye open to eye closed comparison in another study to find patterns associated with comorbid depression in Internet addiction (Lee et al., 2014 ). We have used the same method but added recording during a cognitive task as other studies have reported alternation in the cognitive abilities of IGD patients (Batthyány et al., 2009 ; King et al., 2013 ; Peng & Liu, 2010 ). Moreover, we compared the complexity of brain waves in the three aforementioned states and observed the following differences:

• A significant difference in the opened-eye state at the O2 channel. This alternation of brain wave complexity in the right occipital region has not been previously reported in brain imaging studies.
• A significant difference in the closed-eye state in channel F4; Lin et al. had previously reported alpha band alternation in this channel in addicts (Lin et al., 2010 ).
• A nearly significant difference in the closed-eye state in channel P4. The P4 electrode was placed in the right Parietal and Angular gyri (BA39) and such alternation have been previously reported using fMRI in resting state.
• A nearly significant difference in the closed-eye state in channel F3; previously, Lin et al. had observed alpha band alternation in this channel in addicts (Lin et al., 2010 ).

The differences in P4 and O2 channels suggest asymmetrical differences in the right-sided parieto-occipital areas of the brain in gamers. The P4 area (right 39th Brodman) represents the right angular gyrus which is involved in visuospatial processing. Also, the O2 area (right 18th Brodman) illustrates the right side V2 area, involved in visuospatial information processing. It seems visuospatial processing (activation of the right parieto-occipital area) might be considered as an important neuro marker for gaming addiction.

Limitations

This research is a single-sex study due to the gaming centers in Iran accepting only male players, which can be considered as the limitations of the study. Also, the number of participants and their age range were limited. Moreover, observed differences in the delta pattern, potentially, indicate the early onset of learning disorders, to ensure the occurrence or absence of this complication in addicts people do not affect our results it is better to measure this issue with tests.

Machine-learning methods only show differences between pre-defined groups. They can not provide insights into the physiological mechanisms of the brain and reveal the causal interactions responsible for observations. For example, the complexity changes in the brain waves could be related to better 3-D rotating tasks after gaming. Thus, like other classification studies, this study only proposes that the complexity of the brain waves has changed due to gaming. Hence, it is important to design molecular biology studies to find out the details and reasons for the observations. On the other hand, a longitudinal study should be designed to assess the working memory before and after gaming addiction and find out the changes in brain waves in terms of time.

Brain waves are the result of the brain’s neurodynamic processes. Hence, these changes in brain waves are the signs of a neurophysiological change in the brain. A combination of Higuchi complexity-measure and PartitionMembershipFilter can help classify IGD and normal participants with 95% accuracy as a simple and low-cost method that demonstrates the importance of the brain wave complexity as an EEG data feature. On the other hand, the findings of this study strongly indicate that gaming addiction, as a cognitive disorder, has associated brain wave alterations. Given the ongoing COVID-19 pandemic, people tend to stay more at home and, as such, are in more danger of gaming addiction disorders. Thus, an accurate investigation of cognitive disorders and gaming addiction is vital.

It is a reasonable hypothesis that the dynamics of EEG are inherently random and variations of complexity measures of these signals are either similar or reciprocal with time. To assess the effect of games on this complexity, an experimental case–control study was carried out on computer game addicts and normal individuals in Tehran. EEG data was obtained from each participant in both groups in three steps (resting state, open eye state, and cognitive task), each lasting minutes. The fractal dimensions were extracted using the HFD algorithm and the Partition Membership method. The extracted features were then filtered and combined to prepare the inputs of the support vector machine (SVM) and MLP classifier. Finally, without focusing on any special band, we found statistically significant changes in P4, F3, and F4 channels in the closed-eye state and the O2 channel in the open-eye state. The differences in the brain waves may indicate a pre-existing difference in the gamers’ brains or a neurophysiological change in the brain as a result of playing 3-D games. Further research should be conducted to explore these findings.

Acknowledgements

We would like to thank the staff of the Behju clinic, the gaming club staff, and all participants for their assistance with the collection of data.

Data Availability

Declarations.

We know of no conflicts of interest associated with this publication.

Informed written consent had been obtained from all participants. The study was approved by the ethics committee of the Research Institute for Endocrine Sciences.

As Corresponding Author, I confirm that the manuscript has been read and approved for submission by all the named authors. We declare that this manuscript is original, has not been published before, and is not currently being considered for publication elsewhere.

1 An arithmetic fact is an operation on 2 whole numbers and the correct answer.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Hossein Lanjanian, Email: [email protected] .

Peyman Hassani-Abharian, Email: gro.ssciri@nairahba .

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Online Video Games Addiction Essay

Online video games have already become an essential component of popular culture. A variety of technological products is actively used to train pilots and other safety occupations for better professional skills. Until recently, there was no specific knowledge or empirical evidence with regard to the effect video games may produce on users’ skills. It appears, however, that video games can become a relevant source of better practical knowledge and abilities which young users are to use in the practice. Unfortunately, there is no sufficient information that could unilaterally confirm the positive nature of video games when used by users in practical performance, but the results of recent researches suggest that video games provide unlimited opportunities for the development of better practical skills and avoiding major mistakes (Soukup 43). Video games are often linked to problems including bad grades and violent behavior of people. The idea has been accentuated in the ScienCentral News video reports. The study suggests that might make users do a better job. Confidentiality relates to information sought, obtained, or held by an organization, the disclosure of which might be detrimental to that organization or to the third party that supplied it. In many cases, it is difficult to control e-mail communication between employees and protect information security (Aarseth, 99). The paper will try to answer and research the question “Why people are addicted to online video games? How does it affect their life?”

Online video games create a new reality and can be interested as a “second life” of the user. Bad communications lead to conflict. In this situation, employees cannot find a unified and single solution for the project or program competing with one another during meetings and negotiations. Also, e-mail is not an ideal form to solve current business problems, because it takes time to type an e-mail instead of “simply calling someone if the message is short” (Hartt n.d.). In some cases, employees must accept e-mails inflexibilities, and learn how to interpret the information provided to them and how to make correct deci­sions based on written short messages. On the other hand, electronic mail systems store and then deliver to electronic ‘mail boxes’ which enable the recipient to retrieve the message when convenient.. The main problems that affected e-mail communication include lack of mutual understanding, lack of openness in relations, and damaged relations, chaos situations (Bates, 45). Critics admit that:

Despite this growing concern, children still seem to be spending time playing video games. A recent report released by the Kaiser Family Foundation (1999) reveals that a majority of 2- to 18-year-old children in this country have access to video game technology in their homes. (Smith 54).

As such, whatever is happening in the society in According the theory of the social construction of reality, each person is undeniably a perception and interaction with others. As such, this proves that culture is a vital component that affects the formation of one’s identity. In line with this, the concept of cultural identity was formed. Cultural Identity is often described as an individual’s feeling or perception of his or her belongingness to a certain cultural group. It is also described as the extent to which this feeling affects him or her and influences him/her to act in accordance with the actions, beliefs, traditions, and behavior imposed by the cultural group. A cultural group, on the other hand, refers to a set of individuals, which may be or maybe not be bounded by time and place. However, it is given that members of a cultural group carry the same set of symbolic meanings used in the interpretation of actions and communication. Normally, cultural groups exist in a common cultural space and time also (Berger, 98).

Through the sense of belongingness to a certain online video game, one tries to modify his/her behavior and practices in such a way that they are in accordance with the norms that are accepted by the online video game, to which the individual belongs. Thus, the end product will be that of identity. However, if an individual identifies himself/herself as a member of online video games that have cultural practices that oppose each other, he or she will be exposed to the question of which tradition to follow. Thus, his or her online video game identity will be compromised. In this case, the sense of free will, motivated by which cultural group he/she thinks he/she can more identify with, acts. In the analysis of the cultural identity of the interviewee, the act that he only tries to stay in line with norms of the “majority” while strongly not acting against the norms of his Islam community emphasizes that he identifies himself more as a member of the latter one. However, the fact that the interviewee acknowledges the norms of the “majority” also signifies his appreciation of the culture of this group, and therefore the concept of online video game assimilation exists (Faber, 76).

The changes are far-reaching: the definition of online video game; the nature of the information ‘commons’ for the citizen; the right of privacy in communicated expressions; the regulation of information infrastructures (computer operating systems and networks); the definition of information goods; and the nature of government communication with its citizens. These changes mostly revolve around information ownership and yet no consistent framework has yet to emerge as the question has mostly been approached in a piecemeal way. It is argued in conclusion that a new information dispensation must be built which guarantees information ownership, as this is the foundation on which systems of trading, governance, and research can be built. More interesting, though, was the extent to which users of the Internet as a news source said that as a result, they are using traditional news. It seems that using online video sites may have a more negative effect on news viewing than news reading. This might be because Internet users most often go online for the sort of information featured by television news, especially cable. In the early days, online companies did their very best to replicate the printed or media product (Berners-Lee, 33).

These emergent online video game standards now pose a major problem for competition regulators around the world as they span jurisdictions and the market dominance they create is not easily broken up by their nature. Although in some markets the developers may license the ‘standard’ technology to widen participation (the digital cellular phone standard GSM is one example), in others the standard-setter may aggressively protect its control over the standard as it regards it as an asset. At present most of the dominant information standards have been developed by US companies and they can only be regulated effectively by the US Department of Justice. These technological questions will, however, mark out the information infrastructures of the next century. In the emerging technological and commercial environment defined by the digital encoding of information representations ‘ownership’ is coming to mean different things (Aarseth, 33).

The internet and online video websites have become a new sales channel uncontrolled by the state and free for mass consumers. If any element of the channel can be provided in a more cost-effective way, either by another organization or the application of technology, then the producer will have a strong incentive to change their sales strategy. Mainstream media is limited by censorship and regulations, channels of communication, and geographical scope. In other words, this situation creates a channel conflict for mainstream media. Whenever there are a number of different sales channel elements that can address the same customer base, then there is the potential for conflict. The computer industry is renowned for having multiple channels which often find themselves in direct competition. There have been many instances when the computer manufacture, its distributors and resellers are all fighting for the same business (Smith et al 54).

The online video game is an area occupied by online companies. Some direct marketers have long suspected that the reliance of media advertising on attitudinal factors, instead of behavioral ones, has resulted in “much ado about nothing.” Moreover, they consider the Internet an information media and therefore antithetical to media advertising. When these capabilities are combined to address traditional business situations, it is possible to generate tangible benefits. The American media marketplace is not only larger but far more specialized than any other environment worldwide (Smith et al 54). Also, historically, marketing and advertising, in particular television and electronic media, have had a far more significant role in the United States than elsewhere. These abrupt turns can best be seen through annual changes. In the light of the Internet and its direct potential, these targeting options remained narrow and one-sided. Based on a broadcast model, they made real-time interactivity impossible. Having assessed the degree to which the Internet will affect the organization, the challenge is to manage the adoption of the new technology and the changes it will cause to existing processes. Decisions will be required about the advisability and cost implications of running multiple sales channels and the conflicts that can be created (Aarseth, 66).

Constraints on the geographic, industry, or application areas of trading, available to each channel element, can suddenly disappear. Perhaps the most important thing that can be done is to recognize that a problem will exist and to ensure that the existing channel elements have been informed of how the changes will affect them. If possible, these existing channel partners should be involved in the use of technology and encouraged to accept the changes by sharing part of the planned benefits. There are several reasons why it may be necessary to reduce the level of margin on media products that are being sold via the Internet-related sales channel. It may be sensible to offer a pricing advantage to encourage customers to use the channel. This does not necessarily affect the overall net margin since the cost structure of maintaining the channel may be significantly lower than the traditional alternatives (Berners-Lee 5).

In sum, it was found that people are addicted to online video games because they help users to change their own identity and create the ideal personality they cannot reach in real life. Jones writes that: “As in a video game, in which players acquire new weapons and capabilities within its digital geography and learn more and more about how to play from the collective knowledge of gamers online, both Lost’s characters and its audience are acquiring sequentially the “tools” they need to play. (51). Aarseth states that: “virtual environment has penetrated identity unevenly, thus marketers and advertising use this medium to promote their” (61). These quotes agree that online video games create a new reality for users but do not have a positive and educational impact on their personalities. Lack of regulations and censorship help video websites better position themselves against mainstream media companies. Online video sites succeed in moving economic activity closer to users (viewers) proposing low transaction costs, low barriers to entry, and improved access to information for the consumer. Thus, they have a negative and threatening impact on mainstream media, its audiences, and media messages. (Jones 51). Taking this measure helps the people to engage themselves in doing different kinds of exercises. The same thing can be told about the classic game Asteroids. This game has many important characteristics. One of these characteristics is the ability to rotate the wrist moves of a spaceship. The game is often associated with the waste of time. This is especially true when we are talking about the vast majority of people who are engaged in the research.

Works Cited

Aarseth, E. Cybertext: Perspectives on Ergodic Literature Baltimore and London: Johns Hopkins University Press, 1997.

Bates, B. Game Design: the Art and Business of Creating Games Roseville, CA: Prima Tech (Game Development Series), 2001.

Berger, A.A. Narratives in Popular Culture, Media and Everyday Life Thousand Oaks, CA: Sage, 1997.

Berners-Lee, Tim. Weaving the Web. London: Orion Business Books, 1999.

Faber, L. Re: Play Ultimate Games Graphics London: Laurence King Publishing, 1998.

Jones, S. E. Dickens on Lost: Text, Paratext, Fan-Based Media. Wordsworth Circle , 38 (2007), 51.

Smith, S. L. et al. Popular Video Games: Quantifying the Presentation of Violence and Its Context. Journal of Broadcasting & Electronic Media , 47 (2003), 54.

Soukup, Ch. Mastering the Game: Gender and the Entelechial Motivational System of Video Games. Women’s Studies in Communication , 30 (2007), 43.

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Expert advice on gaming addiction in young people and children

Jason Shiers Dip.Psyh MBACP is a Certified Transformative Coach & Certified Psychotherapist who is Creative Innovations Manager for UK Addiction Treatment.

In 2019, the global games market was worth $152 billion. With growing concerns about the amount of time children and teenagers spend playing online games and the impact it can have, Psychotherapist Jason Shiers, shares his insight on gaming addiction in children.

81% of under 18s regularly play online games and in moderation, gaming can be fun, sociable and interactive with opportunities for children and young people to learn and solve problems. Most will not experience any harm but there are known impacts of gaming addiction in children you need to be aware of:

Gaming addiction and financial concerns

• Social relationships  

Effects of gaming addiction on physical health  

• Effects of gaming addiction on education and personal growth 
• Gaming addiction effect on mental health  

Tips to combat bad gaming habits in children 

• Gaming addiction and financial concerns 
• Effects of gaming on physical health  

Tips to combat bad gaming habits in children

Games that have in-app purchases to buy ‘tokens’ or ‘passes’ could possibly lead to children running up unexpected credit card bills for parents and carers..

To access games, many platforms or developers require credit card details  sometimes even for free downloads. Unless sufficient parental controls are set up – for example, password protection, spending restrictions, and alerts, separate accounts for children or unlinking credit cards from the child’s device – then parents can be stung with big bills for in-game purchases.

• What are in-game and in-app purchases? Although many games are free-to-play, they also include premium features that you have to pay for to access. These could include certain characters, points or virtual currency. So children may use real money in the game to buy these items to enhance their gameplay or progress further in the game.
• Can you get a refund for unauthorised in-game purchases? If your child accidentally spends too much on in-game purchases getting a refund depends on the terms and conditions of the gaming platform or game developer. It’s not always possible to claim the money back – including in cases where parental controls aren’t set up properly.
• One of the biggest gaming sensations, Fortnite , does offer refunds for unauthorised purchases by children – but there’s a limit on how many times they will do this. Other platforms and developers are much less flexible.
• What are loot boxes and what are the risks for children? Loot boxes unlock special features, characters or items in a game. However, they come with a fee and players do not know what’s inside until they have paid. So, you could receive items you really want or nothing of use. Loot boxes have been criticised globally for promoting underage gambling and encouraging multiple purchases. In the UK, there have been calls to ban the sale of loot boxes to children.
• Getting into debt If parents can’t get a refund for unauthorised purchases, they can find themselves with a large debt that is accumulating interest. Sometimes parents insist that their child pays back the debt – including by docking pocket money, reducing spending on other treats or by asking teenagers for contributions from earnings. Children and young adults can also run up large debts from gaming – including students with first-time access to loans and credit cards.
• Legal issues In extreme cases, parents have reported their children to the police for ‘friendly fraud’ . This is usually when they have been unsuccessful in getting a refund for in-game purchases. Although rare, this puts young people at risk of being questioned by the police and even criminalised.

Effects of gaming addiction on education and personal growth

Implications of excessive gaming may result in harmful effects on children’s education and wellbeing..

• Interference with studies – One of the signs of gaming addiction is the impact on other areas of life. If school work is suffering – including boredom in lessons, difficulty concentrating or low motivation to complete homework – then their gaming habits should be assessed.
• Exposure to violent, graphic or sexualised content – Ofcom  found that increasing numbers of parents are concerned about the content of games they play. This includes 25% of parents of 3-4-year-old gamers (compared to 10% in 2017). Most major titles do come with age guidance but as with films or TV shows, many children access the content at a younger age. Fortnite, for example, is rated 12+ – yet many primary school-age children play.

Games with violent, sexualised or highly realistic content (including augmented reality and virtual reality games) can also have an emotional impact on children, especially the younger kids . It’s a controversial area with conflicting research but a study from Science Daily has linked violent video games to aggression in young people.

Social relationships

If gaming is at the expense of connection with friends in real life, then this withdrawal can affect relationship skills in everyday situations..

Gaming can be a social activity. Whether playing with siblings on a console or competing with friends online, there are benefits for social development in gameplay. Increasingly, children and young people are playing games online. In 2018, Ofcom found that three-quarters of 5-15-year-old gamers only ever play online – up from two-thirds in 2017.

Gaming addiction effect on mental health

All of the following elements can indicate gaming addiction. these symptoms tend to be more pronounced when children or young people are not gaming – including if they are prevented from playing..

• Anger or rage – If a parent interrupts a gaming session or broadband goes down, what is the reaction? If children or young people respond with anger or rage – including shouting, screaming or physical attacks, then this is something worth noting.
• Compulsivity – Is there a strong sense of urgency to get back to gaming? Is it difficult to pull yourself away? With children and young people, compulsive play can manifest in playing past switch-off times, late at night or secretively.
• Isolation and loneliness – If children spend long periods of time playing games by themselves, this reduces interaction with relatives and friends in real life. Though many young gamers use online chat in multiplayer games, including to talk to friends in real life – this should be balanced with interactions in the same physical space.
• Depression – In regular gamers, ongoing listlessness, sadness or lethargy can be signs of problem gaming. Depressive symptoms will be most apparent when they are not playing the game – i.e. in the withdrawal phase.

Effects of gaming addiction on physical health

Excessive gaming repeatedly over long periods can potentially cause physical strain on gamers..

• Repetitive strain injury (RSI) Children and young people who play games for extended periods can be affected by RSI. Stiffness, aches, pain and numbness are signs to watch out for. For example, ‘nintendinitis’ refers to thumb, wrist and hand problems associated with playing on gaming consoles. Eye strain is also common if you look at screens for long periods without taking breaks. Screen glare can also affect vision.
• Poor posture If you’re slouching in a chair or you’re hunched over your mobile, then it’s time to take a break. Whilst these positions won’t harm most children immediately, they can lead to serious problems in adulthood.
• Headaches and migraines Headaches may be related to physical causes such as eye strain, bad posture or dehydration. Or they may be related to mental health issues – including anxiety and depression. Young gamers who get regular headaches should get checked out by a doctor.
• Lack of physical activity Playing sedentary games for long periods can mean that people miss out on exercise. The World Health Organisation recommends that children and young people, aged 5 to 17, do at least 60 minutes of activity per day.
• Poor nutrition or self-care When gaming addiction takes over, children and young people may skip meals, rely on junk food, resist taking toilet breaks or have poor hygiene.
• Poor quality sleep Playing stimulating games for many hours at a time, particularly late at night, will make it harder to get to sleep.

It’s understandable to believe that if you can get your child’s gaming under control, then everything will return to normal. However, every addiction is best understood as a symptom rather than the problem. For this reason, telling your child to reduce their gaming, punishing them for breaking rules or restricting their access to devices, probably won’t solve their difficulties permanently.

The key to real change is this – what is so distressing or unsatisfactory about your child’s life when s/he is not gaming? To overcome gaming addiction, your son or daughter will need help to discover the answers to this question, as well as learning how to cope in healthier ways.

Of course, it’s an important step for your child to acknowledge the consequences of harmful gaming, including how health, relationships, education and finances are affected – but this is only the start. Lasting recovery from gaming disorder comes through awareness and emotional resilience . Your child needs to know how to recognise and handle emotional distress including when they crave game play.

More to Explore

See more resources and articles to support children online:

• Advice for 11-13 years
• Advice for 14+ year olds
• Advice for 6-10 years
• Online gaming resources
• Support wellbeing with tech

On site links

• Online gaming – the basics
• Online safety issues
• Just Jack – a positive experience in the digital world
• Top 9 age-specific video games for children to play during holidays

Related Web Links

Learn more about Jason Shiers

Nintendo related injuries and other problems research

Ofcom media use and attitudes report

NHS report on autism and ADHD association with video game addiction

NHS Long Term Plan for children with a gaming addiction

BBC article on gaming addiction debt

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Home — Essay Samples — Entertainment — Video Games — Gaming Addiction

Gaming Addiction

• Categories: Gambling Addiction Video Games

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Published: Nov 7, 2018

Words: 902 | Pages: 2 | 5 min read

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Computer Addiction Summary Essay Example

• Pages: 2 (461 words)
• Published: December 13, 2016
• Type: Essay

Today, parents still worry about the effects of violent computer games and of course it also remains a popular topic for the media. However, with a growing number of children and teens spending excessive time playing computer games, perhaps parents should be more worried about teenagers addicted to computer games than the effects of violent video games. An examination of the literature reveals that those who study video game addiction sometimes disagree on the proportion of children and teenagers addicted to computer games.

However, the majority of studies suggest that approximately 5 to 10% of youth who play computer games become addicted. Compared to other psychological difficulties (such as depression and anxiety), teenage computer game addiction is obviously a relatively new problem faced by families.

As such, parents may lack accurate and/or helpful information on the signs of computer game addiction, the risk factors for video game addiction, and strategies for helping teenagers addicted to computer games after the problem develops.

Computer game addiction is the main problem for the parents and the big question mark for the parents such as why my children can addicted to the games, and what should we do to stop their addiction, nowadays, the children spend their times in the computer for them it’s the games and technology era not the bicycle era again.

The truth is the parents have a big effect for this problem because they are the one who buy the children computer and the one who buy the children a computer games, it is not bad to play computer games, but if too much, it is not good also, the

children need socialize with their friends, they need friends in the real life, not friends in the games world. There are many solutions for the parents to prevent their children’s computer usage.

Parents contribution are very important to help the children who addicted to games, children must know the negative effect because of their addiction, such as: have no friends in the real life, school grade become bad, and the games will effect their health, specially their eyes. Parents can help their children with many way such as use the technology, automatic shut down, use the scheduled task, parents can also indirectly stop the children use the computer too much, use the computer usage as the present if they done their assignment, for the example.

Teens can ask help to their parents to remind them if they’re already spend their time too much in computer and also can ask their parents to hide the mouse or keyboard to prevent them to play computer or indirectly force them to do something else beside playing computer such as go out and play with friends, watch TV, listening to the radio, etc.

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Guest Essay

Mass Tech Layoffs? Just Another Day in the Corporate Blender.

By Ashley Goodall

Mr. Goodall, who previously worked as an executive at Deloitte and at Cisco Systems, is the author of the forthcoming book “The Problem With Change.”

Silicon Valley, home of so many technological and workplace innovations, is rolling out another one: the unnecessary layoff.

After shedding over 260,000 jobs last year, the greatest carnage since the dot-com meltdown more than two decades ago, the major tech companies show little sign of letting up in 2024 despite being mostly profitable, in some cases handsomely so. In their words, the tech companies are letting people go to further the continuing process of aligning their structure to their key priorities , or “transformation” or becoming “ future ready .” Behind these generalities, however, some tech companies are using what has hitherto been an extreme measure in order to engineer a short-term bump in market sentiment.

Investors are indeed thrilled . Meta’s shares are up over 170 percent amid its downsizing talk. And where stock prices go, chief executives will generally follow, which means it is not likely to be long before the unnecessary layoff makes its appearance at another publicly traded company near you.

These layoffs are part of a tide of disruption that is continually churning the work days in corporations everywhere. If you’ve spent any amount of time working at a company of pretty much any size, you’ll be familiar with what I call the resulting “life in the blender”: the unrelenting uncertainty and the upheaval that have become constant features of business life today. A new leader comes in, promptly begins a reorganization and upends the reporting relationships you’re familiar with. Or a consultant suggests a new strategy, which takes up everyone’s time and attention for months until it’s back to business as usual, only with a new mission statement and slideware. Or, everyone’s favorite: A merger is announced and leads to all of these and more.

Now, no business prospers by standing still, and there is no improvement without change. Course corrections, re-orgs and strategic pivots are all necessary from time to time. Technological changes continue to demand the restructuring of major industries. But over the last quarter-century or so, the idea of disruption has also metastasized into a sort of cult, the credo of which holds that everything is to be disrupted, all the time, and that if you’re not changing everything, you’re losing.

You can take courses in disruption at the business schools of Stanford, Cornell, Columbia and Harvard. You can read, on the cover of a leading business magazine, about how to “Build a Leadership Team for Transformation: Your Organization’s Future Depends on It.” And if it is the catechism of chaos you’re after, you can buy the inspirational posters and chant the slogans: Fail fast; disrupt or be disrupted; move fast and break things. Part of this, of course, is a product of the hubris of the Silicon Valley technologists. But part, too, is the belief that the fundamental task of a leader is to instigate change. It is hard to remember a time when there was any other idea about how to manage a company.

Moreover, because a majority of corporate executives — together with the consultants and bankers who advise them, the activist investors who spur them on and the financial analysts who evaluate their efforts — have been raised according to this change credo, the constant churn becomes a sort of flywheel. A leader instigates some change, because that’s what a leader does. The advisers and investors and analysts respond positively, because they’ve been taught that change is always good. There’s a quick uptick in reputation or stock price or both, the executives — paid, remember, mostly in stock — feel they have been appropriately rewarded for maximizing shareholder value, and then everyone moves on to the next change.

But it’s hardly clear that this is having the desired result. Studies of merger and acquisition activity have pegged the rate at which they destroy — rather than increase — shareholder value at something between 60 and 90 percent; a Stanford business school professor, Jeffrey Pfeffer, has argued that layoffs seldom result in lower costs, increased productivity or a remedy for the underlying problems in a business; and few of us who have lived through re-orgs remember them as the occasion for a sudden blossoming of productivity and creativity.

Seen through the eyes of the people on the front lines, the reason for this gap between intent and outcome comes into tighter focus. After all, when the people around you are being “transitioned out,” or when you find yourself suddenly working for a new boss who has yet to be convinced of your competence, it’s a stretch to persuade yourself that all this change and disruption is leading to much improvement at all.

“It’s exhausting,” one person I spoke to about change at work told me. “It’s soul-sucking,” said another. One person told me that after the combination of two departments, his people were like deer in the headlights, unsure of what they should be working on. Another had 19 managers in 10 years. Another told me that perpetual change drained the energy from work: “You say the right things in the meetings, but you don’t necessarily do what needs to be done to make it happen.” Another learned to watch the managers and be alert when they stopped dropping by or communicating: “It is like before a tsunami, when the water goes. You don’t see the water, and then the tsunami comes — all of a sudden, it comes, hard. When everything is calm, I worry.”

Of the dozens of people I spoke to, every single one had some sort of change-gone-bad story to share. And these sorts of reactions are about more than simple frustration or discontent. They are rooted in the psychological response we humans experience when our sense of stability is shattered and our future feels uncertain, and indeed the scientific literature has much light to shed on exactly why life in the blender is so hard on us. Experimenters have found, for example, that our stress is greatest when uncertainty , not discomfort, is at its peak — and uncertainty is the calling card of change at work. Then there is the question of agency: a well-known series of experiments conducted by Steven Maier and Martin Seligman in the 1960s discovered that when we sense we are not in control of a situation we give up trying to make things better — this is “learned helplessness” setting in.

Other researchers have described our fundamental need, as a species, for belonging , and the importance of our social groupings — which helps to explain why we don’t like it when our teams are disassembled, reshuffled and reassembled. And others still have shown that we have — perhaps unsurprisingly! — a deep-seated need for things to make sense in our environment, a need that is so often thwarted by the generic C.E.O. statements and exaggerated cheer-speak with which most change initiatives are communicated.

But while the essential response of the human animal to uncertainty and disruption is hard-wired, the degree of change we introduce into our workplaces isn’t. It’s often a choice. We’ve reached this point because the business world seems to have decided that change is an unalloyed good, and so there is no amount of it that is too much, and no cost of it that is too great.

Were more leaders to be guided by the science of change, or by the stories that people on the front lines share, they would quickly discover that it is stability that is the foundation of improvement. Only once we begin to honor people’s psychological needs at work, by thinking twice before launching into the next shiny change initiative and by paying more heed to the rituals and relationships that allow all of us to point our efforts in a useful direction, can we begin to do justice to the idea that a company must be, first, a platform for human contribution if it is to be anything else at all.

Ashley Goodall, who previously worked as an executive at Deloitte and at Cisco Systems, is the author of the forthcoming book “The Problem With Change.”

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