ICD-116C51	GAMING DISORDER Gaming disorder
ICD-10—	No ICD-10 equivalent
DSM-5-TR—	Internet Gaming Disorder (Section III, condition for further study)

1. Definition and nosology

Gaming disorder (ICD-11 code 6C51) is defined by the World Health Organization as a pattern of persistent or recurrent gaming behaviour — digital gaming or video-gaming, either online or offline — manifested by three core features:

• impaired control over gaming (onset, frequency, intensity, duration, termination, context);
• increasing priority given to gaming to the extent that gaming takes precedence over other life interests and daily activities;
• continuation or escalation of gaming despite the occurrence of negative consequences.

The behaviour pattern must be of sufficient severity to result in significant impairment in personal, family, social, educational, occupational or other important areas of functioning. Note: ICD-11 does not contain a distress criterion for 6C51. Impairment is the obligatory element, and adding “marked distress” as an alternative route into the diagnosis would remove the very feature that gives the category its threshold. The pattern is normally evident over a period of at least 12 months, although the required duration may be shortened if all diagnostic requirements are met and symptoms are severe.

Nosological position

6C51 belongs to the ICD-11 grouping Disorders due to addictive behaviours (6C50–6C5Z), alongside 6C50 Gambling disorder. These are the only two behavioural addictions with dedicated ICD-11 categories; the residual codes 6C5Y (other specified) and 6C5Z (unspecified) exist for other candidate conditions. ICD-11 was adopted unanimously by the 72nd World Health Assembly on 25 May 2019 and entered into force on 1 January 2022.

Subtypes and exclusions

• 6C51.0 — Gaming disorder, predominantly online.
• 6C51.1 — Gaming disorder, predominantly offline.
• 6C51.Z — Gaming disorder, unspecified.

The exclusions listed under 6C51 are Hazardous gaming (QE22), Bipolar type I disorder (6A60) and Bipolar type II disorder (6A61). Excessive gaming occurring exclusively within a manic or hypomanic episode is therefore not coded as 6C51.

The sub-threshold category QE22

QE22 Hazardous gaming, in the ICD-11 chapter on factors influencing health status, denotes a pattern of gaming, online or offline, that appreciably increases the risk of harmful physical or mental health consequences to the individual or to others around the individual, but that does not meet the diagnostic requirements for gaming disorder. It exists precisely so that a pattern warranting professional attention and advice need not be forced into a disorder diagnosis.

The American Psychiatric Association has not recognised gaming as a formal disorder: internet gaming disorder (IGD) remains in DSM-5-TR Section III, “Conditions for Further Study”, and the two criterion sets are described and compared in section 6 of this chapter.

2. History

Pre-history: internet addiction (1990s)

The conceptual precursor was “internet addiction”, proposed as a clinical entity by Young, who adapted DSM-IV pathological gambling criteria to internet use (Young, 1998). Through the 1990s and 2000s gaming was studied mainly inside this broader internet-addiction framework — a conflation later identified as a major source of measurement error, and one that still contaminates systematic reviews of gaming disorder today (Kuss et al., 2017; Colder Carras et al., 2020).

2013 — DSM-5

The APA placed internet gaming disorder in Section III of DSM-5 as a condition requiring further study, explicitly declining to make it an official diagnosis. This status was carried over unchanged into DSM-5-TR in 2022.

2014–2017 — WHO consultations

WHO convened a series of expert meetings on the public-health implications of excessive use of the internet, computers, smartphones and similar devices, and on behavioural addictions. The working-group output that shaped the ICD-11 text argued that gaming disorder displays the core features required of an addictive disorder and that a diagnostic category was needed for people already presenting for treatment (Saunders et al., 2017).

2016–2018 — the scientific controversy

Before ICD-11 was finalised, 26 scholars published an open debate paper opposing inclusion, warning of low research quality, uncritical borrowing from substance-use and gambling criteria, moral panic, and harm to healthy gamers (Aarseth et al., 2017). A partly overlapping group followed with an argument that the burden of evidence for moving from research construct to formal disorder had not been met (van Rooij et al., 2018). Clinicians and public-health researchers replied in the same journal defending inclusion (Griffiths et al., 2017; Rumpf et al., 2018). The substance of this dispute is set out in section 10.

2018–2019 — formal inclusion

The ICD-11 draft containing 6C51 was released in June 2018; the classification was adopted by the 72nd World Health Assembly on 25 May 2019, with entry into force on 1 January 2022. In 2024 WHO published the Clinical Descriptions and Diagnostic Requirements (CDDR) for ICD-11 mental, behavioural and neurodevelopmental disorders, the operational guidance clinicians now use alongside the MMS entry.

Policy context (non-clinical sources)

The following are government and press announcements, not clinical evidence, and no high-quality evaluation of their clinical effect was identified.

• South Korea. The shutdown law (a night-time gaming curfew for minors) was passed on 19 May 2011 and came into force on 20 November 2011. The government announced its abolition on 25 August 2021; the repeal was legislated later that year and the curfew ended from 1 January 2022.
• China. From the National Press and Publication Administration notice of 30 August 2021, users under 18 may play online games only between 20:00 and 21:00 on Fridays, weekends and official holidays — approximately three hours in an ordinary week.
• England and Wales. The NHS National Centre for Gaming Disorders opened in London in October 2019.

3. Epidemiology

Pooled prevalence

Two large meta-analyses converge on a global figure of roughly 3 per cent, which falls substantially once methodological quality is taken into account.

The Stevens confidence interval of 0.19–17.12 per cent must be printed with the point estimate. Quoting 1.96 per cent alone, in a paragraph where every other figure carries an interval, creates a false impression of precision — the stringent-sampling subset is small and the estimate is correspondingly unstable.

The lowest estimates come from Przybylski, Weinstein and Murayama, who applied DSM-5 criteria across four large international cohorts (samples described in secondary sources as drawn from the United States, the United Kingdom, Canada and Germany). More than two of every three people who played games reported no IGD symptoms at all, and the authors found the links between IGD and physical, social and mental health outcomes to be mixed.

Sex distribution

Male preponderance is the most consistent epidemiological finding: 8.5 per cent in males versus 3.5 per cent in females (Kim et al., 2022). Reviews report, citing a meta-analysis by Su and colleagues, that men are more likely than women to show disordered gaming while women are more likely to show problematic social-media use (Musetti et al., 2025).

Age

Prevalence is highest in adolescents and young adults; in Kim et al. (2022) mean sample age was negatively associated with reported prevalence — as were sample size and study quality. In the Singapore panel study of Gentile et al. (2011), which followed 3,034 children in grades 3, 4, 7 and 8 over two years, between 7.6 and 9.9 per cent would be classified as pathological gamers at any single point in time (summarised in the abstract as about 9 per cent).

Two paediatric syntheses are frequently quoted together and should not be: Gentile et al. (2017) report a range of roughly 1 to 9 per cent depending on age, country and sample characteristics, whereas Paulus et al. (2018) report about 2 per cent in representative samples of children and adolescents and a mean prevalence of 5.5 per cent when all samples, including clinical ones, are pooled.

Sources of heterogeneity

Reported prevalence varies by an order of magnitude across studies, and the dominant reason is methodological rather than geographical. In Stevens et al. (2021) the choice of screening tool accounted for 77 per cent of the variance, and adolescent samples, lower cut-off scores and smaller sample size were the significant predictors of higher prevalence. Kim et al. (2022) list region among the moderators influencing heterogeneity; Stevens et al. examined study region as a candidate moderator but did not report it among the significant predictors. A regional difference cannot therefore be asserted on the strength of both meta-analyses, and region remains confounded with instrument, sample type and study quality.

Regional data gap

No peer-reviewed prevalence study of gaming disorder conducted in Azerbaijan was identified in this search, and no ICD-11-based prevalence estimate for the South Caucasus was located. Any local figure must be presented as extrapolation, never as measured data.

4. Aetiology and pathogenesis

No single causal mechanism has been established. The evidence base is dominated by cross-sectional studies; the strongest available data come from longitudinal cohorts and from meta-analyses of risk factors.

Genetics

Direct heritability estimates for ICD-11 gaming disorder do not exist. The closest evidence is a Netherlands Twin Register study of 5,247 monozygotic and dizygotic adolescent twins, in which 48 per cent of the individual differences in compulsive internet use were explained by genetic factors, with identical estimates in boys and girls and the remaining 52 per cent attributable to non-shared environment (Vink et al., 2016).

This figure must not be transferred to gaming disorder. The instrument used, the Compulsive Internet Use Scale, is a 14-item measure of compulsive internet use as a single general construct — loss of control, preoccupation, withdrawal, coping or mood modification, and conflict — and contains no gaming-specific items. Time spent gaming was measured separately in that study and was not the phenotype for which the 48 per cent estimate was obtained.

Theoretical model

The most widely used integrative framework is the I-PACE model (Interaction of Person-Affect-Cognition-Execution), which holds that addictive behaviours emerge from interactions between predisposing person variables, affective and cognitive responses to internal and external cues, and executive and inhibitory control, with reinforcement over time shifting behaviour from gratification-driven to compensation-driven and habitual (Brand et al., 2016; Brand et al., 2019). The model is theoretically influential but has not been validated as a diagnostic or predictive tool, and no clinical decision should rest on it.

Neurobiology

A whole-brain meta-analysis of 40 functional imaging studies, using seed-based d mapping, found that neural alterations in internet gaming disorder are task-dependent (Zheng et al., 2019):

Two points of accuracy matter here. First, the pooled null result in cue-reactivity paradigms concerns the striatum as a whole; a whole-brain analysis of this kind does not license a claim about the ventral striatum specifically. Second, the authors' own conclusion is that the study confirms the critical role of reward and executive-control circuitry in IGD, but not under all conditions — it is not a refutation of reward-system involvement. What the clinician should take from this is narrower and firmer: the frontal findings are not uniformly “hypofrontal” (the right inferior frontal gyrus was hyperactive while the left was hypoactive during risky decision-making), the findings are cross-sectional, and no imaging finding and no biomarker has diagnostic value.

Risk and protective factors — cross-sectional synthesis

The largest synthesis to date pooled 1,586 effects from 253 studies and 210,557 participants published before the COVID-19 pandemic. Empirically robust protective factors were few — self-esteem, intelligence, life satisfaction and education — and all had markedly smaller effect sizes than the risk factors (Ropovik et al., 2023).

Risk and protective factors — longitudinal synthesis

A meta-analysis of 39 longitudinal studies with 37,042 participants gives the most clinically usable structure (Zhuang et al., 2023).

Intrapersonal factors were substantially more predictive than interpersonal or environmental factors. The wide confidence interval on social-media disorder should be noted before that association is used clinically.

Direction of causality

The Singapore two-year panel study is one of the few datasets that can speak to temporal ordering. Greater amounts of gaming, lower social competence and greater impulsivity appeared to act as risk factors for becoming a pathological gamer, whereas depression, anxiety, social phobia and lower school performance appeared to act as outcomes of pathological gaming rather than as its causes (Gentile et al., 2011). The authors' own hedging verb — these variables “seemed to act” in these roles — should be preserved.

Attention-deficit/hyperactivity disorder

A systematic review and meta-analysis found a moderate association between ADHD symptom severity and gaming disorder: r = 0.296 for combined ADHD symptoms, r = 0.306 for inattention and r = 0.266 for hyperactivity (Koncz et al., 2023). This is the single most consistent comorbid association in the field and the highest-yield screening target in practice.

Game design and monetisation

In a survey of 7,422 gamers, spending on loot boxes was associated with problem-gambling severity (η² = 0.054), an association an order of magnitude stronger than for other in-game purchases with real money (η² = 0.004), which suggests specificity for the gambling-like element (Zendle and Cairns, 2018). The design is cross-sectional and the authors state plainly that it cannot distinguish loot boxes acting as a gateway to problem gambling from loot boxes appealing more to people who already gamble problematically.

Competing psychological account

The compensatory internet use model conceptualises excessive gaming as a coping response to unmet psychosocial needs and adverse life situations rather than as a primary addiction (Kardefelt-Winther, 2014). This account competes directly with the addiction model, remains unresolved, and has a practical consequence: in a patient whose gaming escalated in step with a depressive episode or a social crisis, treating the gaming in isolation is unlikely to work.

5. Clinical features

Core presentation

The clinical picture is defined by the three ICD-11 features, not by hours played.

Associated features

Preoccupation with gaming between sessions, irritability, restlessness or dysphoria when gaming is prevented, deception about the extent of gaming, and use of gaming to relieve negative mood are described in the DSM-5-TR criterion set. ICD-11 deliberately does not require tolerance or withdrawal, and the status of withdrawal-like phenomena in gaming remains contested. In practice these features support the diagnosis but cannot carry it.

Functional consequences

In clinical presentations the leading complaints are typically brought by family: falling school performance, school refusal or absenteeism, day–night reversal, withdrawal from offline peers, and conflict escalating around attempts to limit access. The impairment requirement is what separates the disorder from intensive hobby use (Billieux et al., 2019).

Sleep

Night-time gaming, delayed sleep onset and shortened sleep are commonly reported. The American Academy of Sleep Medicine consensus recommendation is 8 to 10 hours of sleep per 24 hours for adolescents aged 13 to 18 years (Paruthi et al., 2016). Evidence linking gaming to sleep disruption is observational, but sleep timing is one of the few functional metrics that can be documented objectively and tracked over treatment.

Physical health

Reported physical correlates include musculoskeletal pain in the back, neck and shoulders, repetitive-strain injuries of the hand and thumb, eye strain with headache and dizziness, and sedentary-lifestyle sequelae. A narrative review notes that four in every ten e-sports athletes report pain, and that gaming for more than five hours a day increased musculoskeletal symptoms among adolescents aged 13 to 18 years (Satish Kumar et al., 2023). The same review states that very few studies exist, that the evidence is mixed, that assessment tools are not validated and that no longitudinal work in adolescents has been done. This is a weak evidence area and must be presented as such.

Comorbidity

ADHD shows the most consistent association (r ≈ 0.30; Koncz et al., 2023). Depression and anxiety are frequently reported, but the synthesis literature underpinning that impression does not survive appraisal: of seven systematic reviews of gaming disorder with depression or anxiety, covering 196 primary studies in total, evidence of selective outcome reporting was found in all seven, only one assessed risk of bias, and exposure definitions were inconsistent — some included studies used internet-addiction rather than gaming-disorder measures. None was classified as reliable, and the authors concluded that systematic reviews related to gaming disorder do not meet methodological standards (Colder Carras et al., 2020). Comorbidity should therefore be described as commonly observed but poorly quantified, and a comorbid mood or anxiety disorder must be assessed and treated on its own merits rather than assumed to be part of the gaming disorder.

Online versus offline subtype

The predominantly online form (6C51.0) is the more frequently described presentation and is associated with multiplayer, persistent-world and competitive titles. The predominantly offline form (6C51.1) is recognised but far less studied.

What is not part of the clinical picture

Long playing hours in a person whose functioning, sleep, relationships and obligations are intact do not constitute gaming disorder. A person-centred study reported in a recent overview identified a “passionate gamer” subgroup who played more than four hours a day and showed low levels of psychological maladjustment (Musetti et al., 2025). The distinction between high involvement and pathological involvement is essential to the validity of the diagnosis (Billieux et al., 2019).

6. Diagnosis

6.1 Unified diagnostic criteria

All of the following must be present:

• impaired control over gaming — onset, frequency, intensity, duration, termination and context;
• increasing priority given to gaming, such that gaming takes precedence over other life interests and daily activities;
• continuation or escalation of gaming despite the occurrence of negative consequences;
• and the pattern results in significant impairment in personal, family, social, educational, occupational or other important areas of functioning.

Duration: the pattern of gaming behaviour and other features are normally evident over a period of at least 12 months, although the required duration may be shortened if all diagnostic requirements are met and symptoms are severe. Specify the subtype: 6C51.0 predominantly online, 6C51.1 predominantly offline, 6C51.Z unspecified.

6.2 How reliable is this diagnosis in practice?

One field study has tested the ICD-11 guidelines directly. In Shanghai and Hebei, 200 gamers aged 15 to 18 years (mean 16.5, 85.6 per cent male) were assessed by 21 rater pairs formed from seven psychiatrists. Inter-rater agreement was κ = 0.545 (95% CI 0.490–0.600) for the ICD-11 gaming disorder guidelines — moderate, by the authors' own Landis and Koch classification — and κ = 0.622 (95% CI 0.553–0.691) for DSM-5 IGD. Agreement between the two systems applied to the same patients was κ = 0.847 (95% CI 0.814–0.880). Clinicians endorsed the guidelines' clinical applicability as quite or extremely good in 86.0 per cent of 400 rating sets (Ma et al., 2021).

The head-to-head comparison of these two kappas is not like-for-like and must not be reported as a clean demonstration that ICD-11 is the less reliable system. The ICD-11 value comes from a three-category judgement (gaming disorder / hazardous gaming / neither), the DSM-5 value from a two-category judgement (IGD yes / no). A three-category kappa is structurally penalised by disagreements over the intermediate hazardous-gaming category, which has no DSM-5 counterpart. Three further limitations belong with the figures: all 200 gamers were screened from a single vocational and technical education centre; there were only seven raters; and the concurrent joint-rater design used in WHO field studies tends to generate higher kappa values than an independent re-interview would, so 0.545 is more likely an optimistic than a pessimistic estimate of everyday practice. There is no replication.

The same study also disposes of a common assumption. Applied to the same 400 rating sets, the ICD-11 criteria yielded slightly more cases (86) than the DSM-5 criteria (78), and agreement between the systems was almost perfect. Differences in reported prevalence between studies therefore cannot be attributed simply to which manual an instrument follows; the instrument itself, its cut-off and the sample matter far more (see section 3).

6.3 Why the 12-month requirement is not arbitrary

Gaming-disorder caseness is markedly unstable over time. A meta-analysis of 50 longitudinal studies found categorical stability of approximately 43 to 45 per cent at one year and 34 to 38 per cent at two years (Sun et al., 2025), and a one-year longitudinal study found substantial movement across the diagnostic threshold in both directions (Hong et al., 2023). A large share of people who cross the threshold at a single assessment will not be above it a year later without any treatment at all. The 12-month requirement is what prevents a transient episode — an examination period, a lost job, a bereavement, a summer holiday with an unlimited connection — from being converted into a psychiatric diagnosis. The permitted shortening applies only when every diagnostic requirement is already met and symptoms are severe; it is not a licence to diagnose early on impression.

6.4 Boundary with normality

Intensive gaming undertaken for skill development, competition or recreation, in the absence of impaired control and of functional impairment, is not a disorder. There is no time threshold in the criteria; hours played are not diagnostic. WHO states in its public guidance that gaming disorder affects only a small proportion of people who engage in digital or video gaming. This is the principal safeguard against false-positive diagnosis (Billieux et al., 2019).

6.5 Diagnostic algorithm

• Step 1. Establish that a gaming pattern is present and characterise it: titles, platform, online or offline, session structure, monetisation exposure including loot boxes.
• Step 2. Assess each of the three core components separately, with concrete behavioural examples rather than self-labelling.
• Step 3. Document functional impairment objectively — school or work records, sleep, family report, loss of previous activities. In the absence of impairment, consider QE22 Hazardous gaming instead.
• Step 4. Establish the 12-month time frame, or document severity sufficient to shorten it.
• Step 5. Exclude a manic or hypomanic episode (6A60, 6A61) as the sole context of the behaviour.
• Step 6. Screen systematically for comorbidity: ADHD first, then depressive disorders, anxiety and social anxiety, substance use and sleep disorders.
• Step 7. Use a validated instrument (GDT, ACSID-11, IGDT-10, IGDS9-SF) as an adjunct only. No instrument is diagnostic on its own.

6.6 ICD-11 and DSM-5-TR compared

The greater emphasis ICD-11 places on impairment is generally read as giving it the higher diagnostic threshold (Musetti et al., 2025).

6.7 Diagnostic stability over one year

In a one-year longitudinal study of 279 participants — 120 patients with problematic gaming and 159 gamers from the general population — 34.7 per cent of participants had a change in DSM-5 IGD diagnosis, while the number of ICD-11 gaming disorder cases increased to 60.4 per cent (Hong et al., 2023). These two figures are different quantities and must not be set against each other as though they were the same: 34.7 per cent is a rate of diagnostic change, whereas 60.4 per cent is the level to which the gaming disorder caseload rose. The authors state explicitly that there was no significant difference between the amounts of IGD and GD diagnostic change over the year, and their interpretive conclusion is the cautious one — that a gaming disorder diagnosis might be more prone to change than an IGD diagnosis. ADHD carried the highest odds ratio for both; the point estimate reported for gaming disorder is of the order of 10⁶ with a correspondingly vast confidence interval and is not interpretable as an effect size, almost certainly reflecting quasi-complete separation in the model.

The clinical consequence is direct: a single cross-sectional assessment establishes a diagnosis at that moment, not a prognosis, and re-assessment is part of management rather than an optional extra.

6.8 Differential diagnosis

One differential question has no settled answer: whether gaming that arises and remits with a mood or attention disorder is a primary condition at all. It remains an acknowledged research priority (Musetti et al., 2025), and in an individual patient it is answered pragmatically — by treating the comorbid disorder properly and observing whether the gaming-related impairment resolves with it.

7. Examination and assessment

General principle

There is no biological test and no imaging finding that can diagnose gaming disorder, and no diagnostic biomarker has been established. Diagnosis rests on clinical interview; psychometric instruments are screening and severity tools only, and none should be used to make or exclude a diagnosis on its own.

Instruments

Critical caveat on instruments

A face-validity evaluation of 29 gaming-disorder screening instruments, comprising an item bank of 417 items rated independently by three professional raters, identified systematic problems of scope, wording and overpathologising — items that capture normal intensive engagement (King et al., 2020). This is not a theoretical concern: in the largest prevalence meta-analysis the choice of screening tool accounted for 77 per cent of the between-study variance (Stevens et al., 2021). An instrument score above a cut-off is a reason to interview carefully, never a diagnosis.

Recommended assessment package

• Structured clinical interview against the three ICD-11 components plus documented functional impairment.
• Collateral history from family or school — essential in minors.
• One ICD-11-aligned instrument (GDT or ACSID-11) for severity tracking over time.
• Systematic comorbidity screening: ADHD first (r ≈ 0.30), then depressive and anxiety disorders, substance use and social anxiety.
• Sleep assessment, with the AASM adolescent recommendation of 8–10 hours as the reference point.
• Assessment of gambling-like monetisation exposure (loot boxes, in-game purchases) and of any co-occurring gambling behaviour.
• Baseline functional metrics for follow-up: school or work attendance and performance, sleep timing, offline social contacts, physical activity.

8. Treatment

There is no approved pharmacotherapy for gaming disorder anywhere. No medication holds FDA approval for this indication, and every agent that has been studied was used off-label. No Cochrane systematic review dedicated to gaming disorder was identified; NICE has published a guideline on gambling-related harms (NG248, 28 January 2025) but none covering gaming disorder; and no WFSBP, CANMAT, NIMH, SAMHSA or VA-DoD clinical guidance specific to gaming disorder was identified. For a formally classified disorder this is a striking gap, and it means the clinician is working from primary trials rather than from guidelines.

Pharmacotherapy: what has actually been studied

The entire pharmacological literature was summarised in a systematic review of 12 clinical trials reporting on 724 participants. Its limitations define what can and cannot be concluded from it:

• 98.6 per cent of participants were male (684 of the 694 for whom sex was reported);
• 100 per cent of the trials were conducted in South Korea;
• 8 of the 12 trials were open-label, and only four studies overall were rated at low risk of bias.

Across all trials, IGD symptom reductions from pre- to post-treatment ranged from 15.4 to 51.4 per cent — a range driven entirely by the bupropion studies (Sá et al., 2023).

Comorbidity is central to reading these figures. Seven of the 12 trials focused primarily on IGD, accounting for 461 participants (63.6 per cent of the total); five of those seven also enrolled participants with psychiatric comorbidity, most commonly ADHD and major depressive disorder, accounting for 298 participants — about 41 per cent of the whole pooled sample. In other words, a substantial share of the observed improvement may belong to the comorbid disorder rather than to the gaming behaviour.

The two most informative individual trials:

• Han and Renshaw (2012). Twelve-week prospective, randomised, double-blind trial (eight weeks active treatment, four weeks follow-up) in 50 men with major depressive disorder and excessive online game play. Bupropion plus internet-use education reduced Young Internet Addiction Scale scores and mean gaming time more than placebo plus education. Clinically relevant detail: during the four-week follow-up the reduction in online game play persisted while depressive symptoms recurred.
• Nam et al. (2017). Twelve-week double-blind comparison of bupropion and escitalopram in patients with major depressive disorder and excessive internet game play. Both groups improved on depressive and IGD symptoms, but with 30 patients in total (15 per arm) the trial cannot support any comparative efficacy claim.

Clinical rule. Pharmacotherapy in gaming disorder is currently directed at comorbidity — ADHD, depression — and not at the gaming behaviour itself. It should be prescribed on the indication of the comorbid disorder, with the gaming outcome monitored rather than promised.

Psychotherapy: cognitive-behavioural therapy is the best-supported option

Three syntheses and one multicentre trial define the position.

• Stevens et al. (2019). Meta-analysis of 12 independent CBT studies. The pre–post effect on IGD symptoms at post-test was g = 0.92 (95% CI 0.50–1.34); depression g = 0.80 (0.21–1.38); anxiety g = 0.55 (0.17–0.93). These are pre–post treatment effects, not controlled between-group comparisons, and the distinction matters because this is the field's headline number. The finding that governs practice is the next one: treatment gains at follow-up were non-significant across all four outcomes — IGD symptoms included, not only depression and anxiety. Gains from CBT are demonstrated in the short term and are not demonstrated to last.
• Wölfling et al. (2019) — the STICA trial. The highest-quality randomised evidence: a multicentre RCT at four outpatient clinics in Germany and Austria, 143 men randomised to manualised short-term CBT (n = 72) or wait-list control (n = 71). Remission occurred in 69.4 per cent of the treatment group versus 23.9 per cent of controls (odds ratio 10.10, 95% CI 3.69–27.65, adjusted for baseline severity, comorbidity, centre and age). Effect sizes at treatment termination: d = 1.19 (AICA-S), d = 0.88 (weekday time online), d = 0.64 (psychosocial functioning), d = 0.67 (depression). The dose was 15 weekly group sessions plus up to eight fortnightly individual sessions — up to about 23 sessions over roughly four months, which is what a service planning to reproduce this result must resource. Limitations: male-only sample, and inclusion was based on internet addiction as the primary diagnosis rather than strictly on ICD-11 gaming disorder.
• Ock et al. (2025). Meta-analysis of 18 RCTs of non-pharmacological interventions (1,950 participants): pooled g = −0.82 (95% CI −1.23 to −0.52; I² = 90.36%). Psychotherapy, mainly CBT, showed the largest effect (10 studies, 1,036 participants; g = −1.34); behavioural interventions g = −0.38; prevention-focused work g = −0.33 versus treatment g = −1.13; adults improved more than adolescents (g = −1.14 versus −0.45). Publication bias was statistically confirmed (Begg p = 0.043; Egger p = 0.002). A further warning sign: the four studies rated at low risk of bias produced the largest effect of all (g = −4.08), an implausible estimate that points to an unstable evidence base rather than to a large true effect.
• Danielsen et al. (2024). The most conservative synthesis — 38 studies, 76 effect sizes, 9,524 participants — yielded a moderate summary estimate, with the authors stating that confidence in the finding is compromised by small-study effects, possible publication bias, a limited study pool and a lack of standardisation.

Brief abstinence

A voluntary 84-hour abstinence protocol in 24 adult gamers, 9 of whom screened positive for IGD, reduced gaming hours, maladaptive gaming cognitions and IGD symptoms, with total compliance and no attrition; at 28 days, 75 per cent of the IGD group showed clinically significant improvement in IGD symptoms and 63 per cent showed reliable improvement in maladaptive cognitions (King et al., 2017). This is a small pilot. Abstinence and controlled-use goals have never been compared in an adequately powered trial, and the treatment goal should be individualised rather than dictated.

Family and developmental interventions

The PIPATIC programme — a manualised six-module individualised psychotherapy for adolescents aged 12 to 18, comprising psychoeducation and motivation, addiction treatment as usual adapted to IGD, intrapersonal, interpersonal, family and new-lifestyle modules, delivered over six months in 22 sessions of about 45 minutes — was compared with standard CBT in two public mental health centres. Both groups showed significant reduction of IGD symptoms (Torres-Rodríguez et al., 2018). The decisive limitation is the sample: 31 adolescents in total across both arms.

Family, parental-mediation, school-engagement and lifestyle measures are widely recommended and are consistent with the longitudinal protective-factor data — peer-relationship quality r = −0.23, school engagement r = −0.18, parent–child relationship r = −0.15, social support r = −0.14 (Zhuang et al., 2023) — but they have not been tested as stand-alone interventions in randomised trials. National regulatory measures such as curfews and weekly time caps for minors exist in several countries; no high-quality evaluation of their effect on clinical outcomes was identified, and the regulation of loot boxes and microtransactions remains an open policy question (Musetti et al., 2025).

Summary of evidence levels

9. Prognosis

Prognostic statements about gaming disorder must be confined to a one- to two-year horizon. Nothing is known beyond it: no follow-up study extending five to ten years was identified, there are no data on the adult outcomes of adolescent-onset gaming disorder, and there are no mortality data.

Natural course

Gaming disorder is not a uniformly chronic condition. A meta-analysis of 50 longitudinal studies found categorical stability of approximately 43 to 45 per cent at one-year follow-up and 34 to 38 per cent at two years — between one third and one half of those who met the threshold at baseline still met it at follow-up, and the remainder did not. Dimensional symptom severity behaved differently: baseline-to-follow-up correlations were positive and statistically significant, indicating moderate to high continuity of severity even where categorical status changed. Stability of this order is similar to that of personality disorder and gambling disorder, and lower than that of schizophrenia or bipolar disorder (Sun et al., 2025). Two qualifiers belong with these figures: the pooled samples were predominantly adolescent, and these are data on untreated natural course.

The one-year longitudinal study of 279 participants points in the same direction: 34.7 per cent had a change in DSM-5 IGD diagnosis over the year, while the number of ICD-11 gaming disorder cases increased to 60.4 per cent, with no significant difference between the amounts of diagnostic change in the two systems (Hong et al., 2023).

Predictors

Prospective risk factors for developing pathological gaming are greater amounts of gaming, lower social competence and greater impulsivity (Gentile et al., 2011). Meta-analytic longitudinal predictors of higher symptom trajectories are gaming time (r = 0.33), loneliness (r = 0.29), co-occurring social-media disorder (r = 0.24), aggression (r = 0.23) and anxiety (r = 0.21); protective predictors are self-control (r = −0.27), conscientiousness (r = −0.25), peer-relationship quality (r = −0.23), self-esteem (r = −0.19) and school engagement (r = −0.18) (Zhuang et al., 2023). ADHD carried the highest odds ratio for diagnostic change in the one-year study (Hong et al., 2023).

Predictors of relapse after successful treatment have not been established. No validated relapse predictor exists, which means that follow-up after remission cannot be targeted at a high-risk subgroup and must be offered on a routine basis.

Outcomes attributable to the disorder

In the two-year Singapore panel, depression, anxiety, social phobia and lower school performance behaved as outcomes of pathological gaming rather than as its causes. Youths who stopped being pathological gamers ended up with lower levels of depression, anxiety and social phobia than those who remained pathological gamers (Gentile et al., 2011). This is a between-group comparison, not a documented within-person improvement, and school performance is not among the variables reported as differing between those who stopped and those who did not. It remains the strongest available evidence that the condition carries prospective harm rather than merely co-occurring with distress.

Treated prognosis

In the STICA multicentre trial, remission occurred in 69.4 per cent of treated men versus 23.9 per cent of wait-list controls (Wölfling et al., 2019). Meta-analysis of non-pharmacological interventions found adults improving substantially more than adolescents (g = −1.14 versus −0.45), an age gradient that should temper prognostic expectations in younger patients (Ock et al., 2025). Against this, the CBT meta-analysis found treatment gains at follow-up to be non-significant across all four outcomes (Stevens et al., 2019): short-term response is well documented, durability is not.

Who is missing from the prognostic evidence

Women and girls are effectively unrepresented in the treatment literature. The one multicentre randomised trial enrolled men only, and 98.6 per cent of participants in the entire pharmacological literature were male. No statement about treatment response or prognosis in female patients can be made from the published evidence, and this should be said to female patients and their families rather than concealed behind figures derived from male samples.

10. Myths and misconceptions

Everyday misconceptions

Myth 1: “Anyone who plays a lot is addicted”

Refuted. ICD-11 contains no time threshold; the diagnosis requires impaired control, displacement of other life interests, continuation despite harm and functional impairment. WHO states that gaming disorder affects only a small proportion of people who engage in digital or video gaming. A person-centred study reported in a recent overview identified a subgroup of “passionate gamers” playing more than four hours a day with low levels of psychological maladjustment (Musetti et al., 2025; Billieux et al., 2019).

Myth 2: “Gaming disorder affects a large share of gamers”

Refuted at the high end. The best-quality estimates are 2.4 per cent in representative samples and 1.4 per cent after adjustment for publication bias (Kim et al., 2022); 1.96 per cent (95% CI 0.19–17.12) in the stringent-sampling subset (Stevens et al., 2021); and 0.3 to 1.0 per cent using DSM-5 criteria in four large international samples (Przybylski et al., 2017).

Myth 3: “A fixed number of hours per day defines the disorder”

Refuted. ICD-11 defines the condition by control, priority, persistence despite harm and impairment. Time is a prospective risk factor (r = 0.33; Zhuang et al., 2023) but it is not a criterion, and it is neither necessary nor sufficient.

Myth 4: “Video games cause violence”

Refuted as a causal claim. The American Psychological Association's February 2020 revision of its resolution states that there is insufficient scientific evidence to support a causal link between violent video games and violent behaviour, and warns explicitly against attributing violence, including mass shootings, to video game use. A reanalysis of the data underlying the APA's 2015 task-force report found negligible relationships with aggressive and prosocial behaviour, small relationships with aggressive affect and cognitions, and stronger relationships with desensitization; effect sizes appeared elevated by non-best practices and researcher-expectancy effects, particularly in experimental studies (Ferguson et al., 2020). The desensitization finding runs against the general argument and is reported here for that reason. Violence and gaming disorder are separate questions and must not be merged.

Myth 5: “Gaming disorder is nothing but a symptom of depression or ADHD”

Partly true and genuinely contested. Comorbidity is high and ADHD shows the most consistent association (r ≈ 0.30; Koncz et al., 2023), and critics argue that disordered gaming is largely secondary to mood and attention problems (van Rooij et al., 2018). Against this, the two-year Singapore panel found depression, anxiety and social phobia behaving as outcomes of pathological gaming rather than as its causes (Gentile et al., 2011). Both readings must be presented; the question is not settled.

Myth 6: “Total abstinence from all games is the only correct treatment goal”

Not established. Brief voluntary abstinence has pilot-level support only (King et al., 2017), and no adequately powered trial has compared abstinence with controlled-use goals. The treatment goal should be individualised.

Myth 7: “There is a medication that cures gaming disorder”

Refuted. No agent has regulatory approval for this indication; the entire pharmacological literature comprises 12 trials, 724 participants, 98.6 per cent male, all conducted in South Korea, with 8 of the 12 open-label (Sá et al., 2023). Medications are used off-label for comorbid ADHD or depression.

Myth 8: “Loot boxes and in-game purchases are harmless entertainment”

Not supported. In 7,422 gamers, loot-box spending was associated with problem-gambling severity (η² = 0.054), an effect an order of magnitude larger than for other in-game purchases (Zendle and Cairns, 2018). The evidence is cross-sectional and does not establish causation in either direction, but the association is robust enough to warrant asking about it in every assessment.

Myth 9: “Gaming disorder is an Asian problem”

Overstated. Prevalence estimates from East and South-East Asian samples tend to be higher, but in the largest meta-analysis the screening tool accounted for 77 per cent of the between-study variance and region was not among the reported significant predictors (Stevens et al., 2021); Kim et al. (2022) list region as one moderator among several. Region is confounded with instrument, sample type and study quality, and cases are reported worldwide.

Myth 10: “The literature on gaming and depression is solid”

Refuted. An appraisal of seven systematic reviews of gaming disorder with depression or anxiety, covering 196 primary studies, found selective outcome reporting in all seven, risk-of-bias assessment in only one, and inconsistent exposure definitions; none was classified as reliable (Colder Carras et al., 2020).

The scientific controversy over the category itself

6C51 is the most contested diagnosis in the ICD-11 addictive-behaviours grouping. The dispute is academic, not commercial, and both of the popular explanations for it — industry lobbying on one side, pure moral panic on the other — are wrong.

The case against inclusion. In 2017, 26 scholars published an open debate paper opposing the WHO proposal (Aarseth et al., 2017). Their arguments were that the research base was of low quality; that the operationalisation borrowed uncritically from substance-use and gambling criteria; that the diagnosis risked pathologising normal, healthy, intensive play; and that formalisation could fuel moral panic, stigmatise a large recreational population and lead to unnecessary treatment of children. A partly overlapping group then argued that the burden of evidence required to move from a research construct to a formal disorder is very high, precisely because diagnoses can be misused, and that WHO should postpone formalisation (van Rooij et al., 2018).

The case for inclusion. The WHO expert-consultation output argued that gaming disorder displays the core features of an addictive disorder and that a category was needed for people already presenting for treatment (Saunders et al., 2017). Rumpf and colleagues replied from a clinical and public-health standpoint: treatment services for these patients already existed in many countries; without a category patients could not be reliably identified, services could not be planned or funded, and research could not be standardised; and the risk of over-diagnosis is mitigated by the strict ICD-11 impairment requirement (Rumpf et al., 2018). Griffiths and colleagues argued that problematic gaming demonstrably exists and constitutes disordered gaming (Griffiths et al., 2017).

WHO's position. WHO maintained the category, framed inclusion as following the independent development of treatment programmes for identical clinical presentations in many parts of the world, and emphasised in its public guidance that gaming disorder affects only a small proportion of people who game — an explicit attempt to defuse the over-pathologising objection.

The industry position. Contrary to a common assumption, the video-game industry opposed the diagnosis. The Entertainment Software Association publicly rejected the addiction framing and urged WHO to reverse direction, and similar concerns were voiced by the European Games Developer Federation (reported in Musetti et al., 2025). The diagnosis cannot be attributed to industry lobbying, and the moral-panic objection came from within academia rather than from the industry.

What remains genuinely unresolved. Three things, each of which changes what a clinician should say to a patient. Inter-rater reliability of the ICD-11 guidelines has been tested once, in one country, in one school-based sample, and was moderate (κ = 0.545). Categorical caseness is unstable over a single year in both diagnostic systems. And the synthesis literature on the commonest comorbidities does not meet accepted methodological standards. None of this makes the diagnosis unusable; all of it makes a confident prognosis unjustified.

11. Sources

1. World Health Organization. ICD-11 for Mortality and Morbidity Statistics: 6C51 Gaming disorder. Geneva: WHO; 2019 (2025-01 release).
2. World Health Organization. ICD-11 for Mortality and Morbidity Statistics: QE22 Hazardous gaming. Geneva: WHO; 2019 (2025-01 release).
3. World Health Organization. Clinical Descriptions and Diagnostic Requirements for ICD-11 Mental, Behavioural and Neurodevelopmental Disorders (CDDR). Geneva: WHO; 2024.
4. World Health Organization. Addictive behaviours: Gaming disorder — Questions and answers. Geneva: WHO.
5. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR). Washington, DC: American Psychiatric Association Publishing; 2022. (Internet gaming disorder — Section III, Conditions for Further Study.)
6. American Psychological Association. APA Resolution on Violent Video Games: February 2020 Revision. Washington, DC: APA; 2020.
7. American Psychological Association. APA reaffirms position on violent video games and violent behavior [press release]. 3 March 2020.
8. National Institute for Health and Care Excellence. Gambling-related harms: identification, assessment and management. NICE guideline NG248. London: NICE; 28 January 2025. (Cited for contrast: no NICE guideline exists for gaming disorder.)
9. Aarseth E, Bean AM, Boonen H, Colder Carras M, Coulson M, Das D, et al. Scholars' open debate paper on the World Health Organization ICD-11 Gaming Disorder proposal. J Behav Addict. 2017;6(3):267–270. doi:10.1556/2006.5.2016.088
10. Billieux J, Flayelle M, Rumpf HJ, Stein DJ. High involvement versus pathological involvement in video games: a crucial distinction for ensuring the validity and utility of gaming disorder. Curr Addict Rep. 2019;6(3):323–330. doi:10.1007/s40429-019-00259-x
11. Brand M, Young KS, Laier C, Wölfling K, Potenza MN. Integrating psychological and neurobiological considerations regarding the development and maintenance of specific Internet-use disorders: an Interaction of Person-Affect-Cognition-Execution (I-PACE) model. Neurosci Biobehav Rev. 2016;71:252–266. doi:10.1016/j.neubiorev.2016.08.033
12. Brand M, Wegmann E, Stark R, Müller A, Wölfling K, Robbins TW, Potenza MN. The Interaction of Person-Affect-Cognition-Execution (I-PACE) model for addictive behaviors: update, generalization to addictive behaviors beyond internet-use disorders, and specification of the process character of addictive behaviors. Neurosci Biobehav Rev. 2019;104:1–10. doi:10.1016/j.neubiorev.2019.06.032
13. Colder Carras M, Shi J, Hard G, Saldanha IJ. Evaluating the quality of evidence for gaming disorder: a summary of systematic reviews of associations between gaming disorder and depression or anxiety. PLoS One. 2020;15(10):e0240032. doi:10.1371/journal.pone.0240032
14. Danielsen PA, Mentzoni RA, Låg T. Treatment effects of therapeutic interventions for gaming disorder: a systematic review and meta-analysis. Addict Behav. 2024;149:107887. doi:10.1016/j.addbeh.2023.107887 (PMID 37826910)
15. Ferguson CJ, Copenhaver A, Markey P. Reexamining the findings of the American Psychological Association's 2015 task force on violent media: a meta-analysis. Perspect Psychol Sci. 2020;15(6):1423–1443. doi:10.1177/1745691620927666
16. Gentile DA, Choo H, Liau A, Sim T, Li D, Fung D, Khoo A. Pathological video game use among youths: a two-year longitudinal study. Pediatrics. 2011;127(2):e319–e329. doi:10.1542/peds.2010-1353
17. Gentile DA, Bailey K, Bavelier D, Brockmyer JF, Cash H, Coyne SM, et al. Internet gaming disorder in children and adolescents. Pediatrics. 2017;140(Suppl 2):S81–S85. doi:10.1542/peds.2016-1758H
18. Griffiths MD, Kuss DJ, Lopez-Fernandez O, Pontes HM. Problematic gaming exists and is an example of disordered gaming. J Behav Addict. 2017;6(3):296–301. doi:10.1556/2006.6.2017.037
19. Han DH, Renshaw PF. Bupropion in the treatment of problematic online game play in patients with major depressive disorder. J Psychopharmacol. 2012;26(5):689–696. doi:10.1177/0269881111400647 (PMID 21447539)
20. Hong YN, Hwang H, Starcevic V, Choi TY, Kim TH, Han DH. Which is more stable and specific: DSM-5 internet gaming disorder or ICD-11 gaming disorder? A longitudinal study. Psychiatry Clin Neurosci. 2023;77(4):213–222. doi:10.1111/pcn.13522 (PMID 36562926)
21. Kardefelt-Winther D. A conceptual and methodological critique of internet addiction research: towards a model of compensatory internet use. Comput Human Behav. 2014;31:351–354. doi:10.1016/j.chb.2013.10.059
22. Kim HS, Son G, Roh EB, Ahn WY, Kim J, Shin SH, Chey J, Choi KH. Prevalence of gaming disorder: a meta-analysis. Addict Behav. 2022;126:107183. doi:10.1016/j.addbeh.2021.107183 (PMID 34864436)
23. King DL, Billieux J, Carragher N, Delfabbro PH. Face validity evaluation of screening tools for gaming disorder: scope, language, and overpathologizing issues. J Behav Addict. 2020;9(1):1–13. doi:10.1556/2006.2020.00001 (PMID 32359228)
24. King DL, Kaptsis D, Delfabbro PH, Gradisar M. Effectiveness of brief abstinence for modifying problematic internet gaming cognitions and behaviors. J Clin Psychol. 2017;73(12):1573–1585. doi:10.1002/jclp.22460 (PMID 28152189)
25. Király O, Sleczka P, Pontes HM, Urbán R, Griffiths MD, Demetrovics Z. Validation of the Ten-Item Internet Gaming Disorder Test (IGDT-10) and evaluation of the nine DSM-5 Internet Gaming Disorder criteria. Addict Behav. 2017;64:253–260. doi:10.1016/j.addbeh.2015.11.005
26. Király O, Bőthe B, et al. Ten-Item Internet Gaming Disorder Test (IGDT-10): measurement invariance and cross-cultural validation across seven language-based samples. Psychol Addict Behav. 2019;33(1):91–103. doi:10.1037/adb0000433
27. Koncz P, Demetrovics Z, Takacs ZK, Griffiths MD, Nagy T, Király O. The emerging evidence on the association between symptoms of ADHD and gaming disorder: a systematic review and meta-analysis. Clin Psychol Rev. 2023;106:102343. doi:10.1016/j.cpr.2023.102343 (PMID 37883910)
28. Kuss DJ, Griffiths MD, Pontes HM. Chaos and confusion in DSM-5 diagnosis of Internet Gaming Disorder. J Behav Addict. 2017;6(2):103–109. doi:10.1556/2006.5.2016.062
29. Ma C, Wang Z, Li C, Lu J, Long J, Li R, Wu Q, Jiang H, Du J, Li R, Wang P, Ma L, Li H, Hui S, Zhao W, Zhong N, Zhao M. The clinical consistency and utility of ICD-11 diagnostic guidelines for gaming disorder: a field study among the Chinese population. Front Psychiatry. 2021;12:781992. doi:10.3389/fpsyt.2021.781992 (PMID 35002801; 781992 is the article number)
30. Müller SM, Wegmann E, Oelker A, Stark R, Müller A, Montag C, Wölfling K, Rumpf HJ, Brand M. Assessment of Criteria for Specific Internet-use Disorders (ACSID-11): introduction of a new screening instrument capturing ICD-11 criteria for gaming disorder and other potential Internet-use disorders. J Behav Addict. 2022;11(2):427–450. doi:10.1556/2006.2022.00013
31. Musetti A, Floros G, Chiappedi M, Stavropoulos V. Gaming disorder in the ICD-11: the state of the game. BMC Psychiatry. 2025;25(1):1114. doi:10.1186/s12888-025-07576-8 (PMID 41272537; 1114 is the article number)
32. Nam B, Bae S, Kim SM, Hong JS, Han DH. Comparing the effects of bupropion and escitalopram on excessive internet game play in patients with major depressive disorder. Clin Psychopharmacol Neurosci. 2017;15(4):361–368. doi:10.9758/cpn.2017.15.4.361 (PMID 29073748)
33. Ock CM, Lee HS, Chae J, Kim H. Effectiveness of non-pharmacological interventions on gaming disorder: a systematic review and meta-analysis. Psychiatry Investig. 2025;22(5):490–503. doi:10.30773/pi.2024.0358
34. Paruthi S, Brooks LJ, D'Ambrosio C, Hall WA, Kotagal S, Lloyd RM, et al. Recommended amount of sleep for pediatric populations: a consensus statement of the American Academy of Sleep Medicine. J Clin Sleep Med. 2016;12(6):785–786. doi:10.5664/jcsm.5866
35. Paulus FW, Ohmann S, von Gontard A, Popow C. Internet gaming disorder in children and adolescents: a systematic review. Dev Med Child Neurol. 2018;60(7):645–659. doi:10.1111/dmcn.13754
36. Pontes HM, Griffiths MD. Measuring DSM-5 Internet gaming disorder: development and validation of a short psychometric scale. Comput Human Behav. 2015;45:137–143. doi:10.1016/j.chb.2014.12.006
37. Pontes HM, Schivinski B, Sindermann C, Li M, Becker B, Zhou M, Montag C. Measurement and conceptualization of Gaming Disorder according to the World Health Organization framework: the development of the Gaming Disorder Test. Int J Ment Health Addict. 2021;19(2):508–528. doi:10.1007/s11469-019-00088-z
38. Poon LYJ, Tsang HWH, Chan TYJ, Man SWT, Ng LY, Wong YLE, Lin CY, Chien CW, Griffiths MD, Pontes HM, Pakpour AH. Psychometric properties of the Internet Gaming Disorder Scale-Short-Form (IGDS9-SF): systematic review. J Med Internet Res. 2021;23(10):e26821. doi:10.2196/26821 (e26821 is the article number)
39. Przybylski AK, Weinstein N, Murayama K. Internet gaming disorder: investigating the clinical relevance of a new phenomenon. Am J Psychiatry. 2017;174(3):230–236. doi:10.1176/appi.ajp.2016.16020224
40. Jahrami H., Husain W., Lin C.-Y. et al. Reliability generalization meta-analysis and psychometric review of the Gaming Disorder Test (GDT): evaluating internal consistency. Addict Behav Rep 2024;20:100563.
41. Ropovik I, Martončik M, Babinčák P, Baník G, Vargová L, Adamkovič M. Risk and protective factors for (internet) gaming disorder: a meta-analysis of pre-COVID studies. Addict Behav. 2023;139:107590. doi:10.1016/j.addbeh.2022.107590 (107590 is the article number)
42. Rumpf HJ, Achab S, Billieux J, Bowden-Jones H, Carragher N, Demetrovics Z, et al. Including gaming disorder in the ICD-11: the need to do so from a clinical and public health perspective. J Behav Addict. 2018;7(3):556–561. doi:10.1556/2006.7.2018.59
43. Sá RRC, Coelho S, Parmar PK, Johnstone S, Kim HS, Tavares H. A systematic review of pharmacological treatments for Internet Gaming Disorder. Psychiatry Investig. 2023;20(8):696–706. doi:10.30773/pi.2022.0297 (PMID 37559452)
44. Satish Kumar CR, Sharma MK, Amudhan S, Arya S, Mahapatra S, Anand N, et al. Digital gaming, musculoskeletal, and related health hazards among adolescents and young adults. Indian J Psychiatry. 2023;65(6):698–700. doi:10.4103/indianjpsychiatry.indianjpsychiatry_818_22
45. Saunders JB, Hao W, Long J, King DL, Mann K, Fauth-Bühler M, et al. Gaming disorder: its delineation as an important condition for diagnosis, management, and prevention. J Behav Addict. 2017;6(3):271–279. doi:10.1556/2006.6.2017.039
46. Stevens MW, Dorstyn D, Delfabbro PH, King DL. Global prevalence of gaming disorder: a systematic review and meta-analysis. Aust N Z J Psychiatry. 2021;55(6):553–568. doi:10.1177/0004867420962851
47. Stevens MW, Dorstyn D, Delfabbro PH, King DL. Corrigendum to: Global prevalence of gaming disorder: a systematic review and meta-analysis. Aust N Z J Psychiatry. 2023;57(6):928. doi:10.1177/00048674221137011 (PMID 36433839)
48. Stevens MWR, King DL, Dorstyn D, Delfabbro PH. Cognitive-behavioral therapy for Internet gaming disorder: a systematic review and meta-analysis. Clin Psychol Psychother. 2019;26(2):191–203. doi:10.1002/cpp.2341
49. Sun AP, Ho CH, Kuss DJ, Cross CL. The temporal stability of problematic gaming and gaming disorder: a systematic review and meta-analysis. Addict Behav Rep. 2025;21:100592. doi:10.1016/j.abrep.2025.100592 (PMID 40125549; 100592 is the article number)
50. Torres-Rodríguez A, Griffiths MD, Carbonell X, Oberst U. Treatment efficacy of a specialized psychotherapy program for Internet Gaming Disorder. J Behav Addict. 2018;7(4):939–952. doi:10.1556/2006.7.2018.111 (PMID 30427213)
51. van Rooij AJ, Ferguson CJ, Colder Carras M, Kardefelt-Winther D, Shi J, Aarseth E, et al. A weak scientific basis for gaming disorder: let us err on the side of caution. J Behav Addict. 2018;7(1):1–9. doi:10.1556/2006.7.2018.19
52. Vink JM, van Beijsterveldt TCEM, Huppertz C, Bartels M, Boomsma DI. Heritability of compulsive Internet use in adolescents. Addict Biol. 2016;21(2):460–468. doi:10.1111/adb.12218
53. Wölfling K, Müller KW, Dreier M, Ruckes C, Deuster O, Batra A, et al. Efficacy of short-term treatment of internet and computer game addiction: a randomized clinical trial. JAMA Psychiatry. 2019;76(10):1018–1025. doi:10.1001/jamapsychiatry.2019.1676 (PMID 31290948)
54. Young KS. Internet addiction: the emergence of a new clinical disorder. CyberPsychology and Behavior. 1998;1(3):237–244. doi:10.1089/cpb.1998.1.237
55. Zendle D, Cairns P. Video game loot boxes are linked to problem gambling: results of a large-scale survey. PLoS One. 2018;13(11):e0206767. doi:10.1371/journal.pone.0206767
56. Zheng H, Hu Y, Wang Z, Wang M, Du X, Dong G. Meta-analyses of the functional neural alterations in subjects with Internet gaming disorder: similarities and differences across different paradigms. Prog Neuropsychopharmacol Biol Psychiatry. 2019;94:109656. doi:10.1016/j.pnpbp.2019.109656 (PMID 31145927)
57. Zhuang X, Zhang Y, Tang X, Ng TK, Lin J, Yang X. Longitudinal modifiable risk and protective factors of internet gaming disorder: a systematic review and meta-analysis. J Behav Addict. 2023;12(2):375–392. doi:10.1556/2006.2023.00017