game addiction literature review

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Internet and Gaming Addiction: A Systematic Literature Review of Neuroimaging Studies

In the past decade, research has accumulated suggesting that excessive Internet use can lead to the development of a behavioral addiction. Internet addiction has been considered as a serious threat to mental health and the excessive use of the Internet has been linked to a variety of negative psychosocial consequences. The aim of this review is to identify all empirical studies to date that used neuroimaging techniques to shed light upon the emerging mental health problem of Internet and gaming addiction from a neuroscientific perspective. Neuroimaging studies offer an advantage over traditional survey and behavioral research because with this method, it is possible to distinguish particular brain areas that are involved in the development and maintenance of addiction. A systematic literature search was conducted, identifying 18 studies. These studies provide compelling evidence for the similarities between different types of addictions, notably substance-related addictions and Internet and gaming addiction, on a variety of levels. On the molecular level, Internet addiction is characterized by an overall reward deficiency that entails decreased dopaminergic activity. On the level of neural circuitry, Internet and gaming addiction led to neuroadaptation and structural changes that occur as a consequence of prolonged increased activity in brain areas associated with addiction. On a behavioral level, Internet and gaming addicts appear to be constricted with regards to their cognitive functioning in various domains. The paper shows that understanding the neuronal correlates associated with the development of Internet and gaming addiction will promote future research and will pave the way for the development of addiction treatment approaches.

1. Introduction

In the past decade, research has accumulated suggesting that excessive Internet use can lead to the development of a behavioral addiction (e.g., [ 1 , 2 , 3 , 4 ]). Clinical evidence suggests that Internet addicts experience a number of biopsychosocial symptoms and consequences [ 5 ]. These include symptoms traditionally associated with substance-related addictions, namely salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse [ 6 ]. Internet addiction comprises a heterogeneous spectrum of Internet activities with a potential illness value, such as gaming, shopping, gambling, or social networking. Gaming represents a part of the postulated construct of Internet addiction, and gaming addiction appears to be the most widely studied specific form of Internet addiction to date [ 7 ]. Mental health professionals’ and researchers’ extensive proposals to include Internet addiction as mental disorder in the forthcoming fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) will come to fruition as the American Psychiatric Association accepted to include Internet use disorder as mental health problem worthy of further scientific investigation [ 8 ].

The excessive use of the Internet has been linked to a variety of negative psychosocial consequences. These include mental disorders such as somatization, obsessive-compulsive and other anxiety disorders, depression [ 9 ], and dissociation [ 10 ], as well as personality traits and pathology, such as introversion and psychoticism [ 11 ]. Prevalence estimates range from 2% [ 12 ] to 15% [ 13 ], depending on the respective sociocultural context, sample, and assessment criteria utilized. Internet addiction has been considered as serious threat to mental health in Asian countries with extensive broadband usage, particularly South Korea and China [ 14 ].

1.1. The Rise of Neuroimaging

In accordance with Cartesian dualism, the French philosopher Descartes advocated the view that the mind is an entity that is separate from the body [ 15 ]. However, the cognitive neurosciences have proved him wrong and reconcile the physical entity of the body with the rather elusive entity of the mind [ 16 ]. Modern neuroimaging techniques link cognitive processes ( i.e. , Descartes’ thinking mind ) to actual behavior ( i.e. , Descartes’ moving body ) by measuring and picturing brain structure and activity. Altered activity in brain areas associated with reward, motivation, memory, and cognitive control has been associated with addiction [ 17 ].

Research has addressed the neural correlates of drug addiction development via classical and operant conditioning [ 18 , 19 ]. It has been found that during the initial stages of the voluntary and controlled usage of a substance, the decision to use the drug is made by specific brain regions, namely the prefrontal cortex (PFC) and ventral striatum (VS). As habituation to use and compulsion develops, brain activity changes in that the dorsal regions of the striatum (DS) become increasingly activated via dopaminergic innervation ( i.e. , dopamine release) [ 20 ]. Long term drug use leads to changes in the brain dopaminergic pathways (specifically the anterior cingulate (AC), orbitofrontal cortex (OFC), and the nucleus accumbens (NAc) which may lead to a reduction of sensitivity to biological rewards and it decreases the individual’s control over seeking and eventually taking drugs. [ 21 , 22 ]. On a molecular level, the long-term depression (LTD; i.e. , the reduction) of synaptic activity has been linked to the adaptation of the brain as a result of substance-related addictions [ 23 ]. Drug addicts become sensitized to the drug because in the course of prolonged intake, the synaptic strength in the ventral tegmental area increases, and so does the LTD of glutamate in the nucleus accumbens, which will result in craving [ 24 ].

At the same time, the brain ( i.e. , NAc, OFC, DLPFC) becomes increasingly responsive to drug cues (e.g., availability, particular context) via craving [ 21 , 25 ]. Craving for drug use involves a complex interaction between a variety of brain regions. Activity in the nucleus accumbens following recurrent drug intake leads to learning associations between drug cues and the reinforcing effects of the drug [ 26 ]. In addition, the orbitofrontal cortex, important for the motivation to engage in behaviors, the amygdala (AMG) and the hippocampus (Hipp), as main brain regions associated with memory functions, play a role in intoxication and craving for a substance [ 17 ].

Natural rewards, such as food, praise, and/or success gradually lose their hedonic valence. Due to habituation to rewarding behaviors and intake of drugs, a characteristic addiction symptom develops ( i.e. , tolerance). Increasing amounts of the substance or increasing engagement in the respective behaviors are needed in order to produce the desired effect. As a result, the reward system becomes deficient. This leads to the activation of the antireward system that decreases the addict’s capacity for experiencing biological reinforcers as pleasurable. Instead, he requires stronger reinforcers, i.e. , their drug or behavior of choice, in larger amounts ( i.e. , tolerance develops) to experience reward [ 27 ]. In addition, the lack of dopamine in the mesocorticolimbic pathways during abstinence explains characteristic withdrawal symptoms. These will be countered with renewed drug intake [ 17 ]. Relapse and the development of a vicious behavioral cycle are the result [ 28 ]. Prolonged drug intake and/or engagement in a rewarding behavior leads to changes in the brain, including dysfunctions in prefrontal regions, such as the OFC and the cingulate gyrus (CG) [ 17 , 29 ].

Research indicates that brain activity alterations commonly associated with substance-related addictions occur following the compulsive engagement in behaviors, such as pathological gambling [ 30 ]. In line with this, it is conjectured that similar mechanisms and changes are involved in Internet and gaming addiction. The aim of this review is therefore to identify all peer-reviewed empirical studies to date that used neuroimaging techniques to shed light upon the emerging mental health problem of Internet and gaming addiction from a neuroscientific perspective. Neuroimaging broadly includes a number of distinct techniques. These are Electroencephalogram (EEG), Positron Emission Tomography (PET), SPECT Single Photon Emission Computed Tomography (SPECT), functional Magnetic Resonance Imaging (fMRI), and structural magnetic resonance imaging (sMRI), such as Voxel-based Morphometry (VBM), and Diffusion-Tensor Imaging (DTI). These are briefly explained in turn before examining the studies that have utilized these techniques for studies on Internet and gaming addiction.

1.2. Types of Neuroimaging Used to Study Addictive Brain Activity

Electroencephalogram (EEG): With an EEG, neural activity in the cerebral cortex can be measured. A number of electrodes are fixed to specific areas ( i.e. , anterior, posterior, left and right) of the participant’s head. These electrodes measure voltage fluctuations ( i.e. , current flow) between pairs of electrodes that are produced by the excitation of neuronal synapses [ 31 ]. With event-related potentials (ERPs), the relationships between the brain and behavior can be measured via an electrophysiological neuronal response to a stimulus [ 32 ].

Positron Emission Tomography (PET): PET is a neuroimaging method that allows for the study of brain function on a molecular level. In PET studies, metabolic activity in the brain is measured via photons from positron emissions ( i.e. , positively charged electrons). The subjected is injected with a radioactive 2-deoxyglucose (2-DG) solution that is taken up by active neurons in the brain. The amounts of 2-DG in neurons and positron emissions are used to quantify metabolic activity in the brain. Thus, neuronal activity can be mapped during the performance of a particular task. Individual neurotransmitters can be distinguished with PET, which makes the latter advantageous over MRI techniques. It can measure activity distribution in detail. Limitations to PET include relatively low spatial resolution, time needed to obtain a scan, as well as potential radiation risk [ 33 ].

Single Photon Emission Computed Tomography (SPECT): SPECT is a subform of PET. Similar to PET, a radioactive substance (a “tracer”) is injected into the blood stream that rapidly travels to the brain. The stronger the metabolic activity in specific brain regions, the stronger the enrichment of gamma rays. The emitted radiation is measured in accordance with brain layers, and metabolic activity is imaged using computerized techniques. Unlike PET, SPECT allows for counting individual photons, however, its resolution is poorer because with SPECT, resolution depends on the proximity of the gamma camera that measures neuronal radioactivity [ 34 ].

Functional Magnetic Resonance Imaging (fMRI): With fMRI, changes in the levels of blood oxygen in the brain are measured that are indicative of neuronal activity. Specifically, the ratio of oxyhemoglobin ( i.e. , hemoglobin that contains oxygen in the blood) to deoxyhemoglobin ( i.e. , hemoglobin that has released oxygen) in the brain is assessed because blood flow in “active” brain areas increases to transport more glucose, also bringing in more oxygenated hemoglobin molecules. The assessment of this metabolic activity in the brain allows for finer and more detailed imaging of the brain relative to structural MRI. In addition to this, the advantages of fMRI include speed of brain imaging, spatial resolution, and absence of potential health risk relative to PET scans [ 35 ].

Structural Magnetic Resonance Imaging (sMRI): sMRI uses a variety of techniques to image brain morphology [ 36 ]. One such technique is Voxel-Based Morphometry (VBM) . VBM is used to compare the volume of brain areas and the density of gray and white matter [ 37 ]. Another sMRI technique is Diffusion-Tensor Imaging (DTI) . DTI is a method used for picturing white matter. It assesses the diffusion of water molecules in the brain which helps to identify interconnected brain structures by using fractional anisotropy (FA). This measure is an indicator of fiber density, axonal diameter, and myelination in white matter [ 38 ].

A comprehensive literature search was conducted using the database Web of Knowledge . The following search terms (and their derivatives) were entered with regards to Internet use: “addiction”, “excess”, “problem”, and “compulsion”. Moreover, additional studies were identified from supplementary sources, such as Google Scholar , and these were added in order to generate a more inclusive literature review. Studies were selected in accordance with the following inclusion criteria. Studies had to (i) assess Internet or online gaming addiction or direct effects of gaming on neurological functioning, (ii) use neuroimaging techniques, (iii) be published in a peer-reviewed journal, and (iv) be available as full text in English language. No time period was specified for the literature search because neuroimaging techniques are relatively new, so that the studies were expected to be recent ( i.e. , almost all having been published between 2000 and 2012).

A total of 18 studies were identified that fulfilled the inclusion criteria. Of those, the method of data acquisition was fMRI in eight studies [ 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ] and sMRI in two studies [ 47 , 48 ], two studies used PET scans [ 49 , 50 ], one of which combined it with an MRI [ 49 ], one used SPECT [ 51 ], and six studies utilized EEG [ 52 , 53 , 54 , 55 , 56 , 57 ]. It should also be noted that two of these were actually the same study with one published as a letter [ 53 ] and one published as a full paper [ 54 ]. One study [ 57 ] met all the criteria but was excluded because the diagnosis details of Internet addiction were insufficient to make valid conclusions. Furthermore, two studies did not directly assess Internet and gaming addiction [ 43 , 50 ], but assessed the direct effects of gaming on neurological activity using an experimental paradigm, and were therefore retained in the review. Detailed information on the included studies are presented in Table 1 .

Included studies.

3.1. fMRI Studies

Hoeft et al . [ 43 ] investigated gender differences in the mesocorticolimbic system during computer-game play among 22 healthy students (age range = 19–23 years; 11 females). All participants underwent fMRI (3.0-T Signa scanner (General Electric, Milwaukee, WI, USA), completed the Symptom Checklist 90-R [ 58 ], and the NEO-Personality Inventory-R [ 59 ]. FMRI was carried out during 40 blocks of either a 24-s ball game with the goal being to gain space or a similar control condition that did not include a specific game goal (as based on its structural makeup). Results indicated that there was an activation of neural circuitries that are involved in reward and addiction in the experimental condition ( i.e. , insula, NAc, DLPFC, and OFC). Consequently, the presence of an actual game goal (a characteristic of most conventional online games that are rule-based rather than pure role-playing games), modified brain activity via behavior. Here, a clear cause and effect relationship is evident, which adds strength to the findings.

Results also showed that male participants had a larger activation (in rNAc, blOFC, rAMG) and functional connectivity (lNAc, rAMG) in the mesocorticolimbic reward system when compared to females. The results furthermore indicated that playing the game activated the right insula (rI; signals autonomic arousal), right dorso-lateral PFC (maximize reward or change behavior), bilateral premotor cortices (blPMC; preparation for reward) and the precuneus, lNAc, and the rOFC (areas involved in visual processing, visuo-spatial attention, motor function, and sensori-motor transformation) compared to the resting state [ 43 ]. The insula has been implicated in conscious craving for addictive substances by implicating decision-making processes involving risk and reward. Insula dysfunction may explain neurological activities indicative of relapse [ 60 ]. Due to its experimental nature, this study was able to provide insight into idiosyncratic brain activation as a consequence of gaming in a healthy ( i.e. , non-addicted) population.

Ko et al . [ 44 ] attempted to identify the neural substrates of online gaming addiction by assessing brain areas involved in urge to engage in online games among ten male online gaming addicts (playing World of Warcraft for more than 30 h a week) compared to ten male controls (whose online use was less than two hours a day). All participants completed the Diagnostic Criteria for Internet Addiction for College Students (DCIA-C; [ 74 ]), the Mini-International Neuropsychiatric Interview [ 75 ], the Chen Internet Addiction Scale (CIAS) [ 71 ], the Alcohol Use Disorder Identification Test (AUDIT) [ 76 ], and the Fagerstrom Test for Nicotine Dependence (FTND) [ 77 ]. The authors presented gaming-related and paired mosaic pictures during fMRI scanning (3T MRscanner), and contrasts in BOLD signals in both conditions were analyzed using a cue reactivity paradigm [ 25 ]. The results indicated cue induced craving that is common among those with substance dependence. There was a dissimilar brain activation among gaming addicts following the presentation of game relevant cues as compared to controls and compared to the presentation of mosaic pictures, including the rOFC, rNAc, blAC, mFC, rDLPFC, and the right caudate nucleus (rCN). This activation correlated with gaming urge and a recalling of gaming experience. It was argued that there is a similar biological basis of different addictions including online gaming addiction. The quasi-experimental nature of this study that artificially induced craving in an experimental and controlled setting allowed the authors to make conclusions as based on group differences, and thus linking online gaming addiction status to the activation of brain areas associated with symptoms of more traditional ( i.e. , substance-related) addictions.

Han et al . [ 42 ] assessed the differences in brain activity before and during video game play in university students playing over a seven-week period. All participants completed the Beck Depression Inventory [ 78 ], the Internet Addiction Scale [ 67 ], and a 7-point visual analogue scale (VAS) to assess craving for Internet video game play. The sample comprised 21 university students (14 male; mean age = 24.1 years, SD = 2.6; computer use = 3.6, SD = 1.6 h a day; mean IAS score = 38.6, SD = 8.3). These were further divided into two groups: the excessive Internet gaming group (who played Internet video games for more than 60 min a day over a 42-day period; n = 6), and general player group (who played less than 60 min a day over the same period; n = 15). The authors used 3T blood oxygen level dependent fMRI (using Philips Achieva 3.0 Tesla TX scanner) and reported that brain activity in the anterior cingulate and orbitofrontal cortex increased among the excessive Internet game playing group following exposure to Internet video game cues relative to general players. They also reported that increased craving for Internet video games correlated with increased activity in the anterior cingulate for all participants. This quasi-experimental study is insightful for it not only offered evidence for a dissimilar brain activity in online gaming addicts compared to a general player control group, but it also elucidated brain activation that occurs as a consequence of playing in both groups. This indicates that (i) craving for online games alters brain activity irrespective of addiction status and might therefore be seen as a (prodromal) symptom of addiction, and that (ii) addicted players can be distinguished from non-addicted online gamers by a different form of brain activation.

Liu et al . [ 45 ] administered the regional homogeneity (ReHo) method to analyze encephalic functional characteristics of Internet addicts under resting state. The sample comprised 19 college students with Internet addiction and 19 controls. Internet addiction was assessed using Beard and Wolf’s criteria [ 72 ]. FMRI using 3.0T Siemens Tesla Trio Tim scanner was performed. Regional homogeneity indicates temporal homogeneity of brain oxygen levels in brain regions of interest. It was reported that Internet addicts suffered from functional brain changes leading to abnormalities in regional homogeneity relative to the control group, particularly concerning the reward pathways traditionally associated with substance addictions. Among Internet addicts, brain regions in ReHo in resting state were increased (cerebellum, brainstem, rCG, bilateral parahippocampus (blPHipp), right frontal lobe, left superior frontal gyrus (lSFG), right inferior temporal gyrus (rITG), left superior temporal gyrus (lSTG) and middle temporal gyrus (mTG)), relative to the control group. The temporal regions are involved in auditory processing, comprehension and verbal memory, whereas the occipital regions take care of visual processing. The cerebellum regulates cognitive activity. The cingulate gyrus pertains to integrating sensory information, and monitoring conflict. The hippocampi are involved in the brain’s mesocorticolimbic system that is associated with reward pathways. Taken together, these findings provide evidence for a change in a variety of brain regions as a consequence of Internet addiction. As this study assessed regional homogeneity under a resting state, it is unclear whether the changes in the brain observed in Internet addicts are a cause or consequence of the addiction. Therefore, no causal inferences can be drawn.

Yuan et al . [ 46 ] investigated the effects of Internet addiction on the microstructural integrity of major neuronal fiber pathways and microstructural changes associated with the duration of Internet addiction. Their sample comprised 18 students with Internet addiction (12 males; mean age = 19.4, SD = 3.1 years; mean online gaming = 10.2 h per day, SD = 2.6; duration of Internet addiction = 34.8 months, SD = 8.5), and 18 non-Internet addicted control participants (mean age = 19.5 years, SD = 2.8). All participants completed the Modified Diagnostic Questionnaire for Internet Addiction [ 72 ], a Self-Rating Anxiety Scale (no details provided), and a Self-Rating Depression Scale (no details provided). The authors employed fMRI and used the optimized voxel-based morphometry (VBM) technique. They analyzed white matter fractional anisotropy (FA) changes by using diffusion tensor imaging (DTI) to discern brain structural changes as a consequence of Internet addiction length. The results showed that Internet addiction resulted in changes in brain structure, and that the brain changes found appear similar to those found in substance addicts.

Controlling for age, gender, and brain volume, it was found that among Internet addicts there was decreased gray matter volume in the bilateral dorsolateral prefrontal cortex (DLPFC), supplementary motor area (SMA), orbitofrontal cortex (OFC), cerebellum and the left rostral ACC (rACC), an increased FA of the left posterior limb of the internal capsule (PLIC), and reduced FA in white matter in the right parahippocampal gyrus (PHG). There was also a correlation between gray matter volumes in DLPFC, rACC, SMA, and white matter FA changes of PLIC with the length of time the person had been addicted to the Internet. This indicates that the longer a person is addicted to the Internet, the more severe brain atrophy becomes. In light of the method, it is unclear from the authors’ description in how far their sample included those who were addicted to the Internet per se , or to playing games online. The inclusion of a specific question asking about the frequency and duration of online gaming (rather than any potential other Internet activity) suggests that the group in question consisted of gamers. In addition to this, the presented findings cannot exclude any other factor that may be associated with Internet addiction (e.g., depressive symptomatology) that may have contributed to the increased severity of brain atrophy.

Dong et al . [ 39 ] examined reward and punishment processing in Internet addicts compared to healthy controls. Adult males ( n = 14) with Internet addiction (mean age = 23.4, SD = 3.3 years) were compared to 13 healthy adult males (mean age = 24.1 years, SD = 3.2). Participants completed a structured psychiatric interview [ 79 ], the Beck Depression Inventory [ 78 ], the Chinese Internet Addiction Test [ 62 , 63 ], and the Internet Addiction Test (IAT; [ 61 ]). The IAT measures psychological dependence, compulsive use, withdrawal, related problems in school, work, sleep, family, and time management. Participants had to score over 80 (out of 100) on the IAT to be classed as having Internet addiction. Furthermore, all those classed as Internet addicts spent more than six hours online every day (excluding work-related Internet use) and had done so for a period of more than three months.

All the participants engaged in a reality-simulated guessing task for money gain or loss situation using playing cards. The participants underwent fMRI with stimuli presented through a monitor in the head coil, and their blood oxygen level dependence (BOLD) activation was measured in relation to wins and losses on the task. The results showed that Internet addiction was associated with increased activation in the OFC in gain trials, and decreased anterior cingulate activation in loss trials compared to normal controls. Internet addicts showed enhanced reward sensitivity and decreased loss sensitivity when compared with the control group [ 39 ]. The quasi-experimental nature of this study allowed for an actual comparison of the two groups by exposing them to a gaming situation and thus artificially inducing a neuronal reaction that was a consequence of the engagement in the task. Therefore, this study allowed for the extrication of a causal relationship between exposure to gaming cues and the resulting brain activation. This may be considered as empirical proof for reward sensitivity in Internet addicts relative to healthy controls.

Han et al . [ 40 ] compared regional gray matter volumes in patients with online gaming addiction and professional gamers. The authors carried out fMRI using a 1.5 Tesla Espree scanner (Siemens, Erlangen) and carried out a voxel-wise comparison of gray matter volume. All participants completed the Structured Clinical Interview for DSM-IV [ 80 ], the Beck Depression Inventory [ 78 ], the Barratt Impulsiveness Scale-Korean version (BIS-K9) [ 81 , 82 ], and the Internet Addiction Scale (IAS) [ 67 ]. Those (i) scoring over 50 (out of 100) on the IAS, (ii) playing for more than four hours per day/30 h per week, and (iii) impaired behavior or distress as a consequence of online game play were classed as Internet gaming addicts. The sample comprised three groups. The first group included 20 patients with online gaming addiction (mean age = 20.9, SD = 2.0; mean illness duration = 4.9 years, SD = 0.9; mean playing time = 9.0, SD = 3.7 h/day; mean Internet use = 13.1, SD = 2.9 h/day; mean IAS scores = 81.2, SD = 9.8). The second group was comprised of 17 professional gamers (mean age = 20.8 years, SD = 1.5; mean playing time = 9.4, SD = 1.6 h/day; mean Internet use = 11.6, SD = 2.1 h/day; mean IAS score = 40.8, SD = 15.4). The third group included 18 healthy controls (mean age = 12.1, SD = 1.1 years; mean gaming = 1.0, SD = 0.7 h/day; mean Internet use = 2.8, SD = 1.1 h/day; mean IAS score = 41.6, SD = 10.6).

The results showed that gaming addicts had higher impulsiveness, perseverative errors, increased volume in left thalamus gray matter, and decreased gray matter volume in ITG, right middle occipital gyrus (rmOG), and left inferior occipital gyrus (lIOG) relative to the control group. Professional gamers had increased gray matter volume in lCG, and decreased gray matter in lmOG and rITG relative to the control group, increased gray matter in lCG, and decreased left thalamus gray matter relative to the problem online gamers. The main differences between the gaming addicts and the professional gamers lay in the professional gamers’ increased gray matter volumes in lCG (important for executive function, salience, and visuospatial attention) and gaming addicts’ left thalamus (important in reinforcement and alerting) [ 40 ]. Based on the non-experimental nature of the study, it is difficult to attribute the evinced dissimilarities in brain structure across groups to the actual addiction status. Possible confounding variables cannot be excluded that may have contributed to the differences found.

Han et al . [ 41 ] tested the effects of bupropion sustained release treatment on brain activity among Internet gaming addicts and healthy controls. All participants completed the Structured Clinical Interview for DSM-IV [ 80 ], the Beck Depression Inventory [ 78 ], the Internet Addiction Scale [ 61 ], and the Craving for Internet video game play was assessed with a 7-point visual analogue scale. Those participants who engaged in Internet gaming for more than four hours a day, scored more than 50 (out of 100) on the IAS, and had impaired behaviors and/or distress were classed as Internet gaming addicts. The sample comprised 11 Internet gaming addicts (mean age = 21.5, SD = 5.6 years; mean craving score = 5.5, SD = 1.0; mean playing time = 6.5, SD = 2.5 h/day; mean IAS score = 71.2, SD = 9.4), and 8 healthy controls (mean age = 11.8, SD = 2.1 years; mean craving score = 3.9, SD =1.1; mean Internet use = 1.9, SD = 0.6 h/day; mean IAS score = 27.1, SD = 5.3). During exposure to game cues, Internet gaming addicts had more brain activation in left occipital lobe cuneus, left dorsolateral prefrontal cortex, and left parahippocampal gyrus relative to the control group. Participants with Internet gaming addiction underwent six weeks of bupropion sustained release treatment (150 mg/day for first week, and 300 mg/day afterwards). Brain activity was measured at baseline and after treatment using a 1.5 Tesla Espree fMRI scanner. The authors reported that bupropion sustained release treatment works for Internet gaming addicts in a similar way as it works for patients with substance dependence. After treatment, craving, play time, and cue-induced brain activity decreased among Internet gaming addicts. The longitudinal nature of this study allows for a determination of cause and effect, which emphasizes the validity and reliability of the presented findings.

3.2. sMRI Studies

Lin et al . [ 48 ] investigated white matter integrity in adolescents with Internet addiction. All participants completed a modified version of the Internet Addiction Test [ 72 ], the Edinburgh handedness inventory [ 83 ], the Mini International Neuropsychiatric Interview for Children and Adolescents (MINI-KID) [ 84 ], the Time Management Disposition Scale [ 85 ], the Barratt Impulsiveness Scale [ 86 ], the Screen for Child Anxiety Related Emotional Disorders (SCARED) [ 87 ], and the Family Assessment Device (FAD) [ 88 ]. The sample comprised 17 Internet addicts (14 males; age range = 14–24 years; IAS mean score = 37.0, SD = 10.6), and 16 healthy controls (14 males; age range = 16–24 years; IAS mean score = 64.7, SD = 12.6). The authors carried out a whole brain voxel-wise analysis of fractional anisotropy (FA) by tract-based spatial statistics (TBSS), and volume of interest analysis was performed using diffusion tensor imaging (DTI) via a 3.0-Tesla Phillips Achieva medical scanner.

The results indicated that the OFC was associated with emotional processing and addiction-related phenomena (e.g., craving, compulsive behaviors, maladaptive decision-making). Abnormal white matter integrity in the anterior cingulate cortex was linked to different addictions, and indicated an impairment in cognitive control. The authors also reported impaired fiber connectivity in the corpus callosum that is commonly found in those with substance dependence. Internet addicts showed lower FA throughout the brain (orbito-frontal white matter corpus callosum, cingulum, inferior fronto-occipital fasciculus, corona radiation, internal and external capsules) relative to controls, and there were negative correlations between FA in the left genu of corpus callosum and emotional disorders, and FA in the left external capsule and Internet addiction. Overall, Internet addicts had abnormal white matter integrity in brain regions linked to emotional processing, executive attention, decision-making and cognitive control compared to the control group. The authors highlighted similarities in brain structures between Internet addicts and substance addicts [ 48 ]. Given the non-experimental and cross-sectional nature of the study, alternative explanations for brain alterations other than addiction cannot be excluded.

Zhou et al . [ 47 ] investigated brain gray matter density (GMD) changes in adolescents with Internet addiction using voxel-based morphometry (VBM) analysis on high-resolution T1-weighted structural magnetic resonance images. Their sample comprised 18 adolescents with Internet addiction (16 males; mean age = 17.2 years, SD = 2.6), and 15 healthy control participants with no history of psychiatric illness (13 males; mean age = 17.8 years, SD = 2.6). All participants completed the modified Internet Addiction Test [ 72 ]. The authors used high-resolution T1-weighted MRIs performed on a 3T MR scanner (3T Achieva Philips), scanned MPRAGE pulse sequences for gray and white matter contrasts, and VBM analysis was used to compare GMD between groups. Results showed that Internet addicts had lower GMD in the lACC (necessary for motor control, cognition, motivation), lPCC (self-reference), left insula (specifically related to craving and motivation), and the left lingual gyrus ( i.e. , areas that are linked to emotional behavior regulation and thus linked to emotional problems of Internet addicts). The authors state that their study provided neurobiological proof for structural brain changes in adolescents with Internet addiction, and that their findings have implications for the development of addiction psychopathology. Despite the differences found between the groups, the findings cannot exclusively be attributed to the addiction status of one of the groups. Possible confounding variables may have had an influence on brain changes. Moreover, the directionality of the relationship cannot be explained with certainty in this case.

3.3. EEG Studies

Dong et al . [ 53 ] investigated response inhibition among Internet addicts neurologically. The recordings of event-related brain potentials (ERPs) via EEG were examined in 12 male Internet addicts (mean age = 20.5 years, SD = 4.1) and compared with 12 healthy control university students (mean age = 20.2, SD = 4.5) while undergoing a go/NoGo task. The participants completed psychological tests ( i.e. , Symptom Checklist-90 and 16 Personal Factors scale [ 89 ]) and the Internet Addiction Test [ 65 ]. The results showed that Internet addicts had lower NoGo-N2 amplitudes (representing response inhibition—conflict monitoring), higher NoGo-P3 amplitudes (inhibitory processes—response evaluation), and longer NoGo-P3 peak latency when compared to controls. The authors concluded that compared to the control group, Internet addicts (i) had lower activation in conflict detection stage, (ii) used more cognitive resources to complete the later stage of the inhibition task, (iii) were less efficient at information processing, and (iv) had lower impulse control.

Dong et al . [ 52 ] compared Internet addicts and healthy controls on event-related potentials (ERP) via EEG while they were performing a color-word Stroop task. Male participants ( n = 17; mean age = 21.1 years, SD = 3.1) and 17 male healthy university students (mean age = 20.8 years, SD = 3.5) completed psychological tests ( i.e. , the Symptom Checklist-90 and the 16 Personal Factors scale [ 89 ]) and the Internet Addiction Test [ 64 ]. This version of the IAT included eight items (preoccupation, tolerance, unsuccessful abstinence, withdrawal, loss of control, interests, deception, escapism motivation) and the items were scored dichotomously. Those participants who endorsed four or more items were classed as Internet addicts. Results showed that Internet addicts had a longer reaction time and more response errors in incongruent conditions compared to controls. The authors also reported reduced medial frontal negativity (MFN) deflection in incongruent conditions than controls. Their findings suggested that Internet addicts have impaired executive control ability compared to controls.

Ge et al . [ 55 ] investigated the association between the P300 component and Internet addiction disorder among 86 participants. Of these, 38 were Internet addiction patients (21 males; mean age = 32.5, SD = 3.2 years) and 48 were healthy college student controls (25 males; mean age = 31.3, SD = 10.5 years). In an EEG study, P300 ERP was measured using a standard auditory oddball task using the American Nicolet BRAVO instrument. All participants completed the Structured Clinical Diagnostic Interview for Mental Disorders [ 80 ], and the Internet Addiction Test [ 64 ]. Those who endorsed five or more (of the eight items) were classed as Internet addicts. The study found that Internet addicts had longer P300 latencies relative to the control group, and that Internet addicts had similar profiles as compared to other substance-related addicts ( i.e. , alcohol, opioid, cocaine) in similar studies. However, the results did not indicate that Internet addicts had a deficiency in perception speed and auditory stimuli processing. This appears to indicate that rather than being detrimental to perception speed and auditory stimuli processing, Internet addiction may have no effect on these specific brain functions. The authors also reported that the cognitive dysfunctions associated with Internet addiction can be improved via cognitive-behavioral therapy and that those who participated in cognitive-behavioral therapy for three months decreased their P300 latencies. The final longitudinal result is particularly insightful because it assessed the development over time that may be attributed to the beneficial effects of therapy.

Little et al . [ 56 ] investigated error-processing and response inhibition in excessive gamers. All participants completed the Videogame Addiction Test (VAT) [ 73 ], the Dutch version of the Eysenck Impulsiveness Questionnaire [ 90 , 91 ], and the Quantity-Frequency-Variability Index for alcohol consumption [ 92 ]. The sample comprised 52 students grouped into two groups of 25 excessive gamers (23 males; scoring more than 2.5 on VAT; mean age = 20.5, SD = 3.0 years; mean VAT score = 3.1, SD = 0.4; average gaming = 4.7 h a day, SD = 2.3) and 27 controls (10 males; mean age = 21.4, SD = 2.6; mean Vat score = 1.1, SD = 0.2; average gaming = 0.5 h a day, SD = 1.2). The authors used a Go/NoGo paradigm using EEG and ERP recordings. Their findings indicated similarities with substance dependence and impulse control disorders in relation to poor inhibition and high impulsivity in excessive gamers relative to the control group. They also reported that excessive gamers had reduced fronto-central ERN amplitudes following incorrect trials in comparison to correct trials and that this led to poor error-processing. Excessive gamers also displayed less inhibition on both self-report and behavioral measures. The strength of this study include its quasi-experimental nature as well as the verification of self-reports with behavioral data. Therefore, validity and reliability of the findings are increased.

3.4. SPECT Studies

Hou et al . [ 51 ] examined reward circuitry dopamine transporter levels in Internet addicts compared to a control group. The Internet addicts comprised five males (mean age = 20.4, SD = 2.3) whose mean daily Internet use was 10.2 h (SD = 1.5) and who had suffered from Internet addiction for more than six years. The age-matched control group comprised nine males (mean age = 20.4, SD = 1.1 years), whose mean daily use was 3.8 h (SD = 0.8 h). The authors performed 99mTc-TRODAT-1 single photon emission computed tomography (SPECT) brain scans using Siemens Diacam/e.cam/icon double detector SPECT. They reported that reduced dopamine transporters indicated addiction and that there were similar neurobiological abnormalities with other behavioral addictions. They also reported that striatal dopamine transporter (DAT) levels decreased among Internet addicts (necessary for regulation of striatal dopamine levels) and that volume, weight, and uptake ratio of the corpus striatum were reduced relative to controls. Dopamine levels were reported to be similar to people with substance addictions and that Internet addiction “may cause serious damages to the brain” ([ 51 ], p. 1). This conclusion cannot be seen as entirely accurate for the directionality of the reported effect cannot be established with the utilized method.

3.5. PET Studies

Koepp et al . [ 50 ] were the first research team to provide evidence for striatal dopamine release during video game play ( i.e. , a game navigating a tank for monetary incentive). In their study, eight male video game players (age range = 36–46 years) underwent positron emission tomography (PET) during video game play and under resting condition. The PET scans employed a 953B-Siemens/CTIPET camera, and a region-of-interest (ROI) analysis was performed. Extracellular dopamine levels were measured via differences in [ 11 C]RAC-binding potential to dopamine D 2 receptors in ventral and dorsal striata. The results showed that ventral and dorsal striata were associated with goal-directed behavior. The authors also reported that the change of binding potential during video game play was similar to that following amphetamine or methylphenidate injections. In light of this, the earliest study included in this review [ 50 ] was already able to highlight changes in neurochemical activity as a consequence of gaming relative to a resting control. This finding is of immense significance because it clearly indicates that the activity of gaming can in fact be compared to using psychoactive substances when viewed from a biochemical level.

Kim et al . [ 49 ] tested whether Internet addiction was associated with reduced levels of dopaminergic receptor availability in the striatum. All participants completed the Structured Clinical Interview for DSM-IV [ 80 ], the Beck Depression Inventory [ 93 ], the Korean Wechsler Adult Intelligence Scale [ 94 ], the Internet Addiction Test [ 69 ] and the Internet Addictive Disorder Diagnostic Criteria (IADDC; [ 68 ]). Internet addiction was defined as those participants who scored more than 50 (out of 100) on the IAT, and endorsed three or more of the seven criteria on the IADDC.

Their sample comprised five male Internet addicts (mean age = 22.6, SD = 1.2 years; IAT mean score = 68.2, SD = 3.7; mean daily Internet hours = 7.8, SD = 1.5) and seven male controls (mean age = 23.1, SD = 0.7 years; IAT mean score = 32.9, SD = 5.3; mean daily Internet hours = 2.1, SD = 0.5). The authors carried out a PET study and used a radiolabeled ligand [ 11 C]raclopride and positron emission tomography via ECAT EXACT scanner to test dopamine D 2 receptor binding potential. They also performed fMRI using a General Electric Signa version 1.5T MRI scanner. The method for assessing D 2 receptor availability examined regions of interest (ROI) analysis in ventral striatum, dorsal caudate, dorsal putamen. The authors reported that Internet addiction was found to be related to neurobiological abnormalities in the dopaminergic system as found in substance-related addictions. It was also reported that Internet addicts had reduced dopamine D 2 receptor availability in the striatum ( i.e. , bilateral dorsal caudate, right putamen) relative to the controls, and that there was a negative correlation of dopamine receptor availability with Internet addiction severity [ 49 ]. However, from this study it is unclear to what extent Internet addiction may have caused the differences in neurochemistry relative to any other confounding variable, and, similarly, whether it is the different neurochemistry that may have led to the pathogenesis.

4. Discussion

The results of the fMRI studies indicate that brain regions associated with reward, addiction, craving, and emotion are increasingly activated during game play and presentation of game cues, particularly for addicted Internet users and gamers, including the NAc, AMG, AC, DLPFC, IC, rCN, rOFC, insula, PMC, precuneus [ 42 , 43 ]. Gaming cues appeared as strong predictors of craving in male online gaming addicts [ 44 ]. Moreover, it was shown that associated symptoms, such as craving, gaming cue-induced brain activity, and cognitive dysfunctions can be reduced following psychopharmacological or cognitive-behavioral treatment [ 41 , 55 ].

In addition to this, structural changes have been demonstrated in Internet addicts relative to controls, including the cerebellum, brainstem, rCG, blPHipp, right frontal lobe, lSFG, rITG, lSTG, and mTG. Specifically, these regions appeared to be increased and calibrated, indicating that in Internet addicts, neuroadaptation occurs that synchronizes a variety of brain regions. These include, but are not limited to, the widely reported mesocorticolimbic system involved in reward and addiction. In addition, Internet addicts’ brains appear to be able to integrate sensorimotor and perceptual information better [ 45 ]. This may be explained by a frequent engagement with Internet applications such as games, which require a stronger connectivity between brain regions in order for learned behaviors and reactions to addiction-relevant cues to occur automatically.

Furthermore, compared to controls, Internet addicts were found to have decreased gray matter volume in the blDLPFC, SMA, OFC, cerebellum, ACC, lPCC, increased FA lPLIC, and decreased FA in white matter in the PHG [ 46 ]. The lACC is necessary for motor control, cognition, and motivation, and its decreased activation has been linked to cocaine addiction [ 95 ]. The OFC is involved in processing emotions and it plays a role in craving, maladaptive decision-making processes, as well as the engagement in compulsive behaviors, each of which are integral to addiction [ 96 ]. Moreover, the length of Internet addiction correlated with changes in DLPFC, rACC, SMA, and PLIC, testifying to the increase of brain atrophy severity over time [ 46 ]. The DLPFC, rACC, ACC, and PHG have been linked to self-control [ 22 , 25 , 44 ], whereas the SMA mediates cognitive control [ 97 ]. Atrophy in these regions can explain the loss of control an addict experiences in regards to his drug or activity of choice. The PCC, on the other hand, is important in mediating emotional processes and memory [ 98 ], and a decrease in its gray matter density may be indicative of abnormalities associated with these functions.

The increase of the internal capsule has been linked to motor hand function and motor imagery [ 99 , 100 ], and can possibly be explained by the frequent engagement in computer games, that requires and significantly improves eye-hand coordination [ 101 ]. Moreover, decreased fiber density and white matter myelination as measured with FA were found in the anterior limb of the internal capsule, external capsule, corona radiation, inferior fronto-occipital fasciculus and precentral gyrus in Internet addicts relative to healthy controls [ 48 ]. Similar white matter abnormalities have been reported in other substance-related addictions [ 102 , 103 ]. Similarly, fiber connectivity in the corpus callosum was found to be decreased in Internet addicts relative to healthy controls, which indicates that Internet addiction may have similar degenerative consequences with regards to links between the hemispheres. These findings are in accordance with those reported in substance-related addictions [ 104 ].

Moreover, there appeared gender differences in activation in such a way that for males, the activation and connectivity of brain regions associated with the mesocorticolimbic reward system were stronger relative to females. This may explain the significantly higher vulnerability for males to develop an addiction to gaming and the Internet that has been reported in reviews of the empirical literature ( i.e. , [ 7 , 105 ]).

In addition to the MRI findings, the EEG studies assessing Internet and gaming addiction to date offer a variety of important findings that may help in understanding behavioral and functional correlates of this emergent psychopathology. In addition to this, the experimental nature of all of the included EEG studies allows for the determination of a causal relationship between the assessed variables. It has been shown that compared to controls, Internet addicts had decreased P300 amplitudes and an increased P300 latency. Typically, this amplitude reflects attention allocation. The differences in amplitude between Internet addicts and controls indicate that either Internet addicts have an impaired capacity for attention or they are not able to allocate attention adequately [ 55 , 57 ]. Small P300 amplitudes have been associated with genetic vulnerability for alcoholism in a meta-analysis [ 106 ]. Decreased P300 latency furthermore was found to distinguish heavy social drinkers from low social drinkers [ 107 ]. Accordingly, there appears to be a common change in neuronal voltage fluctuations in persons addicted to substances and the engagement in Internet use relative to people who are not addicted. Accordingly, Internet addiction appears to have an effect on neuroelectric functioning that is similar to substance addictions. Generally, Internet addicts’ brains appeared to be less efficient with regards to information processing and response inhibition relative to healthy control participants’ brains [ 54 , 56 ]. This indicates that Internet addiction is associated with low impulse control, and the use of an increased amount of cognitive resources in order to complete specific tasks [ 53 ]. Furthermore, Internet addicts appear to have an impaired executive control ability relative to controls [ 56 , 53 ]. These results are in accordance with reduced executive control ability found in cocaine addicts, implicating decreased activity in pre- and midfrontal brain regions that would allow for impulse-driven actions [ 108 ].

From a biochemical point of view, the results of PET studies provide evidence for striatal dopamine release during gaming [ 50 ]. Frequent gaming and Internet use were shown to decrease dopamine levels (due to decreased dopamine transporter availability) and lead to neurobiological dysfunctions in the dopaminergic system in Internet addicts [ 49 , 51 ]. The decreased availability was linked with the severity of Internet addiction [ 49 ]. Reduced dopamine levels have been reported in addictions time and again [ 26 , 109 , 110 ]. Furthermore, structural abnormalities of the corpus striatum have been reported [ 51 ]. Damages to the corpus striatum have been associated with heroin addiction [ 111 ].

The studies included in this literature review appear to provide compelling evidence for the similarities between different types of addictions, notably substance-related addictions and Internet addiction, on a variety of levels. On the molecular level, it has been shown that Internet addiction is characterized by an overall reward deficiency that is characterized by decreased dopaminergic activity. The direction of this relationship is yet to be explored. Most studies could not exclude that an addiction develops as a consequence of a deficient reward system rather than vice versa. The possibility that deficits in the reward system predispose certain individuals to develop a drug or a behavioral addiction such as Internet addiction may put an individual at greater risk for psychopathology. In Internet addicts, negative affectivity can be considered the baseline state, where the addict is preoccupied with using the Internet and gaming to modify his mood. This is brought about by the activation of the antireward system. Due to the excessive use of the Internet and online gaming, opponent processes appear to be set in motion that quickly habituate the addict to the engagement with the Internet, leading to tolerance, and, if use is discontinued, withdrawal [ 27 ]. Accordingly, decreased neuronal dopamine as evinced in Internet addiction may be linked to commonly reported comorbidities with affective disorders, such as depression [ 112 ], bipolar disorder [ 113 ], and borderline personality disorder [ 10 ].

On the level of neural circuitry, neuroadaptation occurs as a consequence of increased brain activity in brain areas associated with addiction and structural changes as a consequence of Internet and gaming addiction. The cited studies provide a clear picture of Internet and gaming addiction pathogenesis and stress how maladaptive behavioral patterns indicative of addiction are maintained. The brain adapts to frequent use of drugs or engagement in addictive behaviors so that it becomes desensitized to natural reinforcers. Importantly, functioning and structure of the OFC and cingulate gyrus are altered, leading to increased drug or behavior salience and loss of control over behaviors. Learning mechanisms and increased motivation for consumption/engagement result in compulsive behaviors [ 114 ].

On a behavioral level, Internet and gaming addicts appear to be constricted with regards to their impulse control, behavioral inhibition, executive functioning control, attentional capabilities, and overall cognitive functioning. In turn, certain skills are developed and improved as a consequence of frequent engagement with the technology, such as the integration of perceptual information into the brain via the senses, and hand-eye coordination. It appears that the excessive engagement with the technology results in a number of advantages for players and Internet users, however to the detriment of fundamental cognitive functioning.

Taken together, the research presented in this review substantiates a syndrome model of addictions for there appear to be neurobiological commonalities in different addictions [ 115 ]. According to this model, neurobiology and psychosocial context increase the risk to become addicted. The exposure to the addictive drug or behavior and specific negative events and/or the continued use of the substance and engagement in the behavior leads to behavioral modification. The consequence is the development of full-blown addictions, that are different in expression (e.g., cocaine, the Internet and gaming), but similar in symptomatology [ 115 ], i.e. , mood modification, salience, tolerance, withdrawal, conflict, and relapse [ 6 ].

Notwithstanding the insightful results reported, a number of limitations need to be addressed. First, there appear methodological problems that may decrease the strength of the reported empirical findings. The reported brain changes associated with Internet and online gaming addiction described in this review may be explained in two different ways. On the one hand, one could argue that Internet addiction leads to brain alterations relative to controls. On the other hand, people with unusual brain structures (as the ones observed in the present study) may be particularly predisposed to developing addictive behaviors. Only experimental studies will allow a determination of cause and effect relationships. Given the sensitive nature of this research that essentially assesses potential psychopathology, ethical considerations will limit the possibilities of experimental research in the field. In order to overcome this problem, future researchers should assess brain activity and brain alterations on a number of occasions during a person’s life longitudinally. This would allow for the extrication of invaluable information with regards to the relationships of pathogenesis and related brain changes in a more elaborate and, importantly, causal fashion.

Secondly, this review included neuroimaging studies of both Internet addicts and online gaming addicts. Based on the collected evidence, it appears difficult to make any deductions as regards the specific activities the addicts engaged in online, other than some authors specifically addressing online gaming addiction. Others, on the other hand, used the categories Internet addiction and Internet gaming addiction almost interchangeably, which does not allow for any conclusions with regards to differences and similarities between the two. In light of this, researchers are advised to clearly assess the actual behaviors engaged in online, and, if appropriate, extend the notion of gaming to other potentially problematic online behaviors. Ultimately, people do not become addicted to the medium of the Internet per sé, but it is rather the activities that they engage in that may be potentially problematic and could lead to addictive online behavior.

5. Conclusions

This review aimed to identify all empirical studies to date that have used neuroimaging techniques in order to discern the neuronal correlates of Internet and gaming addiction. There are relatively few studies ( n = 19), and therefore it is crucial to conduct additional studies to replicate the findings of those already carried out. The studies to date have used both structural and functional paradigms. The use of each of these paradigms allows for the extrication of information that is crucial for establishing altered neuronal activity and morphology as precipitated by Internet and gaming addiction. Overall, the studies indicate that Internet and gaming addiction is associated with both changes in function as well as structure of the brain. Therefore, not only does this behavioral addiction increase the activity in brain regions commonly associated with substance-related addictions, but it appears to lead to neuroadaptation in such a way that the brain itself actually changes as a consequence of excessive engagement with the Internet and gaming.

In terms of the method, neuroimaging studies offer an advantage over traditional survey and behavioral research because, using these techniques, it is possible to distinguish particular brain areas that are involved in the development and maintenance of addiction. Measurements of increased glutamatergic and electrical activity give insight into brain functioning, whereas measures of brain morphometry and water diffusion provide an indication of brain structure. It has been shown that each of these undergoes significant changes as a consequence of Internet and gaming addiction.

To conclude, understanding the neuronal correlates associated with the development of addictive behaviors related to using the Internet and playing online games will promote future research and will pave the way for the development of addiction treatment approaches. In terms of clinical practice, increasing our knowledge regarding the pathogenesis and maintenance of Internet and gaming addiction is essential for the development of specific and effective treatments. These include psychopharmacological approaches that target Internet and gaming addiction specifically on the level of biochemistry and neurocircuitry, as well as psychological strategies, that aim to modify learned maladaptive cognitive and behavioral patterns.

Conflict of Interest

The authors declare no conflict of interest.

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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.

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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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DOI : https://doi.org/10.1038/s41598-022-24523-9

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

1. Introduction

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

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

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

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

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

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

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

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

5. Discussion

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

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

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

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

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A Systematic Review of the Co-occurrence of Gaming Disorder and Other Potentially Addictive Behaviors

• Technology Addiction (J Billieux, Section Editor)
• Open access
• Published: 07 September 2019
• Volume 6 , pages 383–401, ( 2019 )

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• Tyrone L. Burleigh 1 ,
• Mark D. Griffiths 1 ,
• Alex Sumich 2 ,
• Vasileios Stavropoulos 3 &
• Daria J. Kuss 1  

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Purpose of Review

The playing of videogames has become an everyday occurrence among many adolescents and emerging adults. However, gaming can be problematic and potentially addictive and problematic gamers can experience co-occurring behavioral or substance use-related problems. The aims of the present review were to (i) determine the co-occurrence of potentially addictive behaviors with problematic and disordered gaming, and (ii) elucidate the potential risk factors in the development and maintenance of co-occurrence within disordered gaming.

Recent Findings

The main findings demonstrated that there are few empirical studies ( N  = 20) examining (i) co-occurrence of gaming disorder with other addictive behaviors; (ii) longitudinal risk of disordered gaming with co-occurring addictive behaviors; and (iii) mechanisms of co-occurrence in disordered gaming with co-occurring potentially addictive behaviors. Results suggest that disordered gaming can co-occur with a variety of other addictive behaviors (e.g., alcohol use disorder or addictive use of social media), and that research into the co-occurrence of addictive behaviors and substance use is increasing.

Based on this systematic review, findings suggest that gamers engage in a number of potentially addictive behaviors and substance use which can have detrimental effects on health and wellbeing. While a majority of the reviewed studies consider prevalence rates from a range of geographical locations, there are fewer papers which investigate individual and environmental risk factors.

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Introduction

Research has begun to investigate the negative consequences of problematic video gaming in an effort to improve screening, assessment, definition, and treatment of the disorder [ 1 ]. Such work has contributed to the American Psychiatric Association (APA) [ 2 ] including Internet Gaming Disorder (IGD) as a form of behavioral addiction (warranting further investigation) in the latest (fifth) edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) in “Section 3” (“Emerging measures and models”). The World Health Organization [ 3 ] has also recognized “gaming disorder” (GD) as an official disorder with addiction like properties in the eleventh revision of the International Classification of Diseases (ICD-11).

Prior to the inclusion of GD in the DSM-5 and ICD-11, several other terms were used to describe problematic video gaming including videogame addiction, pathological video gaming, gaming use disorder, and gaming use dependency [ 4 , 5 , 6 , 7 ]. Further confusing the issue, online problematic gaming has also been included within the umbrella terms of internet addiction, problematic internet use, and pathological internet use [ 8 , 9 , 10 ]. However, the internet addiction umbrella term encompasses several other problematic online activities, such as online gambling, online sex, social media use, and online shopping [ 11 ]. In order to maintain consistency throughout the present review, the term “disordered gaming” will be used to describe a range of similar and/or overlapping addictive, compulsive, and/or problematic gaming behaviors. When referring to clinically defined cases, the term “GD” will be used, in line with DSM-5 and ICD-11. Furthermore, in relation to other potentially addictive behaviors, the term “problematic” will be used to describe subclinical conditions that do not fully meet all the criteria in the DSM-5 or ICD-11 (e.g., problematic gambling), while the term “disordered” will be used to describe clinical conditions that meet the requisite criteria in the DSM-5 and ICD-11.

There has been a growing body of research suggesting that disordered gaming is associated with a number of other mental health disorders, such as depression [ 12 ], anxiety [ 11 ], problematic substance use [ 13 ], and personality disorders [ 14 ]. However, an understudied area in this field is the co-occurrence of disordered gaming with other potentially addictive substances and behaviors. Within the present review, co-occurrence refers to when two or more potentially addictive behaviors (behavioral and/or substance) are engaged in concurrently. For example, in a systematic review on the prevalence of eleven different types of addictions, it was estimated that approximately 10% of adults with internet addiction may experience another concurrent problematic behavior or substance use (e.g., alcohol use or dependence or gambling addiction [ 15 ]).

Evidence supports the co-occurrence of addiction for both substances and behaviors (i.e., the presence of a behavioral addiction increases the propensity for addiction to develop for other behaviors). Indeed, this may create a cycle of reciprocity, wherein mutual exacerbation occurs between two or more problematic behaviors [ 16 , 17 , 18 ]. Moreover, those who do experience co-occurring problematic and addictive behaviors are at higher risk of poor mental health (e.g., depression) and physical health [ 18 , 19 , 20 ].

In addition, co-occurring problematic behaviors interact to exacerbate clinical symptoms, which can complicate accurate assessment and treatment of other psychiatric disorders [ 21 ]. Likewise, disordered gaming may mask problematic substance use which could hinder diagnostic assessment. Alternatively, disordered gaming may exacerbate problematic substance use, causing symptoms of both to alternate which can impact treatment efficacy [ 22 ]. This highlights that the assessment and treatment of GD should have a broader focus by not only considering the presenting primary problematic behavior or substance use and symptoms, but also any potential co-occurring addictive behaviors or substance use, which may enforce a cycle of reciprocity.

Consequently, clinicians need to be aware of how potentially addictive behaviors impact or enforce various aspects of a primary problematic behavior (e.g., disordered gaming), and be aware of how co-occurring addictions may impact the onset, course, and outcomes of interventions. Previous literature has demonstrated that the prevalence of co-occurring addictions can be high [ 15 ], suggesting that studies which consider addiction as only comprising one specific behavior may be limited in ecological invalidity because individuals have more complex and varied histories of disordered behaviors and co-occurrence [ 17 ].

Although there has been one previous comprehensive review investigating the co-occurrence of eleven behavioral and substance addictions, this mainly evaluated US studies, did not examine disordered gaming, and was written almost a decade ago [ 15 ]. Furthermore, this review was limited to clinical measures in relation to co-occurrence, and did not consider any proxy measures (e.g., time spent engaging in the activity as an indication of problematic or disordered behavior). Consequently, given the large increase in research examining disordered gaming in the past decade, there is a need for a contemporary systematic review examining the co-occurrence of GD with other potentially addictive behaviors. While several studies have considered the impact of co-occurrence of neurodevelopmental and mood disorders on the onset, course, and maintenance of GD [ 23 ], there is limited integrative research examining addiction comorbidities. Furthermore, failure to integrate treatments which consider co-occurring addictions may lead to a “ping pong effect,” wherein an individual may bounce back and forth between problematic or disordered behaviors and/or substance use and treatment programs [ 24 ].

There are several studies within the behavioral and substance addiction literature that support the efficacy and benefits of treating co-occurring addictions concurrently [ 24 , 25 ]. Therefore, in order to integrate contemporary research, it is important to conduct a systematic review highlighting extant findings concerning the co-occurrence of addictive behaviors, which specifically considers problematic and disordered gaming and not the often-used broader construct of “internet addiction.” This may aid in the development of effective models that identify and aid clinicians to treat disordered gaming alongside other co-occurring addictive behaviors.

The primary goal of the present study was to review empirical research over the past decade, providing up-to-date information that considers the impact of addiction to other behaviors on GD, and to provide recommendations for future research. More specifically, the aims of the present review were to (i) determine the co-occurrence of potentially addictive behaviors with problematic and disordered gaming, and to (ii) elucidate the potential risk factors in the development and maintenance of co-occurrence within GD.

A systematic review was employed to examine the co-occurrence of potentially addictive behaviors with disordered gaming. While disordered gaming has been conflated with internet addiction in the past, it is important to note that only papers that considered assessed gaming and/or gaming disorder (i.e., problematic gaming) were considered. A systematic review contains key elements, such as an overview of the literature, summary of the findings, dissemination of outcomes, and identification of gaps in the literature [ 26 ]. The present review utilized a five-stage model of conducting a rigorous systematic review, which included (i) identifying the research question, (ii) identifying relevant studies, (iii) study selection, (iv) dissemination of outcomes, and (v) summarizing and reporting the results [ 26 ].

The inclusion criteria for the present review were as follows: (i) empirical studies containing primary data, (ii) studies that assessed the co-occurrence of and potential “cross-addiction” or “addiction hopping” within the problematic or disordered gaming literature; (iii) studies published in peer-reviewed journals, (iv) written in English, and (v) published within the past decade. ProQuest, Scopus , and Web of Science were searched, including the following databases : PsychARTICLES, PsychINFO, Scopus, Web of Science Core Collection, and MEDLINE . The search included a number of terms related to disordered gaming that have been used over the past decade. In addition to this, several terms were developed to explore cross-addiction and co-occurrence in the behavioral addiction literature, which led to the following search strategy: (patholog* OR problem* OR addict* OR compulsive OR dependen* OR disorder* OR excess*) AND (video gam* OR computer gam* OR internet gam* OR online gam*) AND (“cross addiction” OR “addiction hopping” OR “expression hopping” OR “substitution hypothesis” OR “switching hypothesis” OR “co-occur*” OR comorbid* OR “dual diagnosis”). Each study’s title, abstract, and paper content were screened for eligibility. The full texts of potentially relevant studies were retrieved and screened for eligibility.

A total of 4160 papers were identified in the initial search. The ProQuest database contained 2507 papers ( PsychARTICLES n  = 1749; PsychINFO n  = 799); Scopus contained 1271 papers; and Web of Science contained 341 papers. Duplicate studies were removed, leaving a total of 3915 papers. These papers had their journal of publication, titles, and abstracts screened, resulting in the exclusion of 3845 papers that were not relevant to the present review, leaving a total of 70 papers, which were eligible for further review. Of these, 54 were excluded as they were not written in English ( n  = 3), did not asses disordered gaming ( n  = 15), did not assess cross-addiction or co-occurrence ( n  = 16), did not consider disordered gaming in conjunction with another behavioral or substance addiction/disorder ( n  = 17), or were review papers ( n  = 3). The remaining 16 papers were considered eligible for further analysis as they met all the inclusion criteria. Furthermore, four additional relevant papers were included from the reference lists of the identified papers, bringing the total to 20 papers. The present paper followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA statement; Moher, Liberati, Tetzlaff, Altman, & the PRISMA Group, 2009), which includes the use of a PRISMA flow diagram (see Fig. 1 ).

Flow diagram of paper selection process for the systematic review

The 20 papers that met the inclusion criteria were divided into specific categories. A total of 16 papers were considered as papers that had assessed co-occurrence prevalence of problematic or disordered gaming with other addictive behaviors and had explored their commonalities with various related and/or unrelated risk and/or protective factors. Of these 16 papers, ten were categorized as “prevalence of co-occurrence in GD and other potentially addictive behaviors.” The papers within this category each featured validated psychometric measures which provided an indication of severity risk for disordered gaming and other potentially addictive behaviors. The other six papers that assessed prevalence were categorized as “proxy indicators of GD prevalence and other potentially addictive behaviors.” Unlike the papers in the first category, these papers did not use psychometric measures as a tool to assess severity for both problematic or disordered gaming and the co-occurring problematic or disordered behavior and/or substance use. Instead, these studies assessed the frequency of the behavior (e.g., sexual activity; “how many times have you engaged in sexual activity in the last week?”) or the consumption of substance (e.g., number of alcoholic drinks; “How many alcoholic drinks have you had in the past week?”) as an indicator of use and assessment. The remaining four papers were categorized as “assessing the etiology of disordered gaming and other potentially addictive behaviors.” These papers investigated specific relationships between GD and the mechanisms which may contribute to the understanding of the development, maintenance, or exacerbation of GDs with other potentially addictive behaviors (e.g., coping strategies and personality factors).

Prevalence of Gaming Disorder Co-occurrence with Other Addictive Behaviors

Of the ten studies examining prevalence [ 10 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ], six examined adult populations (e.g., general populations), three examined adolescent populations (e.g., middle school students; see Table 1 ), and one examined both adolescents and adults. Six [ 27 , 28 , 29 , 30 , 32 , 35 ] of these studies focused on the co-occurrence of GD with problematic substance use (i.e., nicotine and cannabis use) and alcohol use. While there has been some exploration of other potentially addictive behaviors, such as buying, phone use, eating, gambling, exercise, sexual behavior, and social media use, these were usually a part of a larger investigation of substance use or disordered substance use, which did not consider disordered gaming as the primary focus. Consequently, their findings lack nuanced consideration of disordered gaming and the wider implications within the gaming studies field. Four studies [ 10 , 31 , 34 , 80 ] investigated the co-occurrence of GD with other “technological addictions” (e.g., social media addiction and internet addiction).

Prevalence was investigated in eight geographical locations, including Norway ( n  = 2) [ 33 , 35 •], Hungary ( n  = 1) [ 10 ], Netherlands ( n  = 1) [ 28 ], the USA ( n  = 2) [ 27 , 32 ], Italy ( n  = 1) [ 34 ], Germany ( n  = 1) [ 29 ], South Korea ( n  = 1) [ 30 ], and Portugal ( n  = 1) [ 31 ]. Sample sizes ranged from 128 to 21,053 participants. However, the type of surveyed populations was relatively narrow, with the majority of the studies considering school students ( n  = 5), and to a lesser degree the general population ( n  = 3) (see Table 1 ).

Six studies investigated the prevalence of problematic or disordered gaming within adult populations. Lee et al. [ 27 ] investigated the relationship between attention-deficit hyperactivity disorder (ADHD), cigarette smoking, problematic gaming, and the frequency of playing videogames in an online American adult sample ( N  = 2801). Their results suggested that ADHD, cigarette smoking, and frequency of playing videogames had a significant impact on problematic gaming. This finding was consistent with previous studies, such as Ream et al.’s study [ 32 ], who found a significant correlation with nicotine, alcohol, caffeine, cannabis use, and problematic videogame use in a large American sample of adult gamers ( N  = 2885). Furthermore, among gamers, 64% used caffeine and 41% of those caffeine users had consumed caffeine while gaming; 26% of their sample used nicotine and 61% of smokers had smoked cigarettes while gaming; 34% of participants consumed alcohol, and 38% of those had drank alcohol while gaming; and 5.6% of their sample smoked cannabis and 80% of those had smoked cannabis while gaming.

Similarly, Na et al. [ 30 ] surveyed South Korean adults ( N  = 1819) online, and found that 21% experienced both problematic alcohol use (i.e., scoring over 20 on the Korean version of Alcohol Use Disorders Identification test [AUDIT-K]) and problematic gaming. This group also had higher cigarette smoking rates (44.8%) than participants in the problematic alcohol group (31.6%) or problematic gaming group (26%), which is consistent with the American sample above. Furthermore, their results indicated that those participants who reported both drinking alcohol and gaming demonstrated higher scores on psychometric tests (which indicated poorer mental health outcomes) than any other group (i.e., control, alcohol group, and gaming group), lending support to the notion that co-occurring substance use and activities and potentially addictive behaviors are associated with maladaptive clinical outcomes [ 30 ].

Müller et al. [ 29 ] investigated exercise dependence (EXD) in a German sample of participants attending a fitness center ( N  = 128). Their results found that out of the ten males (7.8%) who were at risk of developing EXD, two experienced problematic gaming. One participant was at risk of an eating disorder and at risk of problematic gaming, while the other had problematic alcohol use and problematic gaming, and at-risk pathological buying. While this example is not statistically significant, it does illustrate that problematic gaming can co-occur with other potentially addictive behaviors. Moreover, the research indicates that problematic or disordered gaming does not always co-occur with problematic substance use.

In a Norwegian university sample ( N  = 5217), Erevik et al. [ 35 •] reported that 44.9% of participants who had low engagement in gaming were more likely to experience the co-occurrence of problematic alcohol use than those who did not play video games (46.1%; however, this difference became non-significant when controlling for demographic variables, personality, and mental health), while the 4% of participants who experienced high levels of videogame engagement were found to be less likely to experience problematic alcohol use. A larger Norwegian online survey by Andreassen et al. [ 36 ] sampled 25,533 participants and found that 7% experienced problematic gaming and 13.5% experienced problematic social media use. Furthermore, there was a positive association between symptoms of problematic gaming and problematic social media use, demonstrating common risk factors (e.g., impulsive personality, comorbid psychopathology) and the potential for co-occurrence. This finding was corroborated in a study by Monacis et al. [ 34 ] which considered the commonalities in shared identity styles in co-occurring online behaviors. In their sample of university students ( N  = 445) aged over 20 years, they found that social media addiction and GD shared common identity styles (i.e., informational and diffuse-avoidant), further demonstrating the potential for these problematic behaviors to co-occur.

However, disordered gaming and problematic substance use are not limited to the adult population. Similar results have been found in adolescent populations. For example, in a large survey of 21,053 Dutch adolescents by Mérelle et al. [ 28 ], 5.7% of the sample reported some problematic gaming (5.7%) and 9.1% reported problematic social media use. Smoking cigarettes was strongly associated with problematic gaming. Although their results suggested a high co-occurrence of problematic social media use and smoking cigarettes with problematic gaming, there was a weak association with other substance use.

Pontes [ 31 ] investigated how disordered gaming and social media addiction uniquely contributed to psychological distress, and how these behaviors exacerbate distress when they co-occurred in a population of Portuguese middle school students ( N  = 700). The results demonstrated that both disordered gaming and social media addiction can exacerbate the symptoms of each other when they co-occur and contribute to deterioration of psychological health as indicated by increased scores on depression, anxiety, and stress scales. In Király et al.’s nationally representative study [ 10 ], of 2073 adolescents, 4.3% experienced problematic gaming, 8.8% experienced problematic internet use, and 6.7% experienced both problematic videogame use and internet use. Their results demonstrated an overlap in problematic internet use and problematic gaming but verified that these are two distinct problematic behaviors that have the potential to co-occur with one another, and which may lead to the exacerbation of problematic internet use and/or problematic gaming [ 31 ].

Proxy Indicators of Prevalence of Gaming Disorder and Other Potentially Addictive Behaviors

Other studies have focused on prevalence of disordered gaming and other potentially addictive behaviors using proxy indicators (e.g., using number of alcoholic drinks consumed per day or per week to assess severity of alcohol use). Of the six studies that assessed proxy measures of potentially addictive behaviors [ 42 , 82 , 83 , 84 , 85 , 86 , 87 ] (Table 2 ), two studies [ 84 , 99 ] examined general adult populations (e.g., national surveys) using proxy indictors of problematic use, two [ 85 , 86 ] considered both adolescents and adults, while the latter two [ 82 , 87 ] examined adolescent populations (e.g., secondary school students). A total of five of six studies [ 82 , 83 , 84 , 86 , 87 ] using proxy measures investigated alcohol use and substance use, while four considered smoking cigarettes [ 82 , 84 , 86 , 87 ], and one investigated gambling [ 85 ]. The geographical locations also varied with papers based in the USA ( n  = 2) [ 83 , 87 ], Italy ( n  = 1) [ 82 ], Canada ( n  = 1) [ 84 ], the Czech Republic ( n  = 1) [ 86 ], and France ( n  = 1) [ 85 ].

In regard to prevalence within the adolescent populations, two studies showed a positive correlation between the frequency of video game use and substance use, demonstrating a strong association [ 82 , 87 ]. More specifically, Gallimberti et al. [ 82 ] found in their adolescent sample ( N  = 1156) that 16.4% experienced problematic gaming, and within this cohort, 41.2% had smoked cannabis, 23.2% had consumed an energy drink (i.e., caffeine), 21.7% had smoked a cigarette (i.e., nicotine), and 21.3% had drank alcohol (in their lifetime), demonstrating an association between gaming and use of these substances.

Van Rooij et al. [ 87 ] also suggested that higher scores on the Video Game Addiction Test (VGAT; which assesses problematic videogame use) indicated an increase in frequency of substance use. Their research showed that 36.4% of online gamers in their sample ( n  = 8478) consumed alcohol, 34% smoked cigarettes, and 44.6% smoked cannabis. This is in line with studies that exclusively used psychometric measures to asses use and severity of other potential addictions. A similar trend was found in a large sample of Czech online gamers ( N  = 3952) [ 86 ] which investigated gamers and the influence of psychoactive substances. They found that while gaming, caffeine was the most frequently used substance (74.2%), followed by alcohol (40.4%), nicotine (25.3%), and illicit substances (14.5%).

Similarly, Konkolÿ Thege et al. [ 84 ] surveyed 6000 adults and found that those who experienced problematic gaming (2.1%), 1.2% experienced problematic alcohol use, while 31.1% experienced problematic nicotine use, and 13.5% experienced problematic cannabis use. This was calculated using a single self-report question “Thinking back over your life, have you ever personally had a problem with [problematic behavior or substance use]?” with 3 possible responses—“No,” “Yes, but not in the past 12 months,” and “Yes, in the past 12 months.” Using this question, the researchers also considered potentially addictive behaviors that co-occur with disordered gaming. Their results suggested that 37.2% of their participants had experienced the co-occurrence of problematic work, 36.6% had experienced problematic eating behaviors (i.e., eating too little or too much), 14.1% had experienced problematic sex (i.e., excessive sexual behavior), and 12.3% had experienced problematic gambling. The latter finding was in line with a study by McBride et al. [ 85 ], which reported that 11.4% of disordered gamers within in their sample experienced problem gambling, and which is consistent within the wider literature [ 100 , 101 ]. Finally, a study by Ivory et al. [ 83 ] on US college students ( n  = 533) suggested that gaming was not significantly associated with nicotine or substance use. However, taken as a whole, the aforementioned studies tend to indicate that disordered gaming appears to frequently co-occur alongside problematic substance use, and there are complex associations between the co-occurring problematic substance use and potential behavioral addictions.

Assessing the Etiology of Gaming Disorder and Co-occurring Potentially Addictive Behaviors

Four [ 7 , 33 , 102 , 103 ] out of the 20 eligible studies identified for review were classified as etiological papers and defined as papers that attempted to explore the underlying mechanisms that may contribute to co-occurrence of GD with other potentially addictive behaviors and possible etiological pathways (see Table 3 ). These papers also have diverse geographical locations, including Norway [ 33 ], Spain [ 7 ], Australia [ 102 ], and Germany [ 103 ].

Dysfunctional coping strategies have been used to explore how underlying cognitive mechanisms contribute to the development and maintenance of co-occurring behavioral and substance addictions and understand etiology. Schneider et al. [ 102 ] utilized the Brief COPE [ 126 ] to assess different subdomains of coping styles (i.e., a range of cognitive and behavioral responses that are utilized in stressful situations) [ 127 ]. They surveyed 823 Australian high school students ( M  = 14.3, SD = 1.4) and found that coping may play a pivotal role when considering co-occurring risk behaviors. They highlighted a tendency toward denial and behavioral disengagement coping styles—which were positively correlated with substance use—within those who scored higher on disordered gaming, suggesting that adolescents may employ avoidant coping strategies.

In a sample of 472 Spanish students (aged 13–21 years), Estévez et al. [ 7 ] assessed the relationship between emotional regulation and attachment in several addictive behaviors, including disordered gaming. The study found that attachment style was predictive of behavioral addictions, but not substance addictions. Poor peer attachment predicted gaming and gambling disorders, and poor maternal attachment predicted problematic internet use.

With regard to personality, Andreassen et al. [ 36 ] found that social media addiction, internet addiction, and disordered gaming were all negatively associated with conscientiousness among a small sample of Norwegian university students ( n  = 218). Walther, Morgenstern and Hanewinkel [ 103 ] also proposed that co-occurrence between substance and behavioral addictions could be explained via personality traits. Their results indicated that impulsivity and social anxiety were associated with substance users, gamblers, and gamers. The high impulsiveness trait (i.e., doing things without thinking them through) characterized individuals who engage in problematic substance use, problematic gambling, and problematic gaming. However, while low social anxiety was predictive of problematic substance use and problematic gambling, the reverse was true for problematic gaming, where those with high social anxiety were at higher risk for problematic gaming behavior. It should also be noted that social anxiety has been associated with dysfunctional coping strategies ( [ 102 ], which in turn has been implicated in addiction [ 128 , 129 ]. Furthermore, the researchers noted that while problematic substance users have high co-occurrence to other addictions, each addiction to one substance showed associations with personality traits (i.e., high impulsivity and high extraversion) and mental health problems (e.g., high depression, low social anxiety). Problem gamers showed overlap in some of these traits (i.e., impulsivity and social anxiety) with problematic gamblers.

The aim of the present paper was to review and describe the literature on co-occurrence within the field of gaming disorder (GD) published over the past decade. The review considered the prevalence rates in empirical studies that investigated the potential co-occurrence of potential behavioral addictions and/or substance use in those with GD. It also described the use of psychometrically validated assessment instruments and proxy measures in assessing prevalence rates, as well as the etiological studies that investigated the development and maintenance of co-occurrence of potentially addictive behaviors among those with GD.

Ten papers considered GD and a co-occurring potential behavioral addictions and/or substance use and employed validated psychometric measures to assess the prevalence, frequency, and severity of the behaviors studied. Six papers investigated adult populations [ 27 , 29 , 30 , 32 , 36 , 130 ], four papers investigated adolescents [ 10 , 28 , 31 ], and one considered both [ 34 ].

Ream et al. [ 32 ] investigated a North American sample and found that of those who consume psychoactive substances (e.g., nicotine and/or coffee) also engaged in concurrent use of gaming. The surveyed literature also suggested that smoking nicotine or drinking alcohol can have an impact on problematic gaming scores [ 27 , 30 ]. The broader literature suggests an overlap between various substance and behavioral addictions, suggesting it is a relatively common occurrence [ 15 ] among adults. Collectively, the reviewed literature also demonstrates that adults who play video games engage in concurrent use of psychoactive substances, which may result in co-occurring problematic use and engagement in potentially addictive behaviors.

The surveyed literature on adolescents also reflects a range of prevalence rates. In a nationally representative Hungarian sample, it was shown that 4.3% experienced problematic gaming, 8.8% experienced problematic internet use, and 6.7% experienced both problematic gaming and internet use [ 84 ]. Andreassen et al.’s [ 36 ] results suggest that 7% of Norwegian adults reported problematic gaming. A similar result was found among a Dutch sample, which reported 5.7% of their sample experienced some problematic gaming and 9.1% reported problematic social media use, both of which were strongly associated with nicotine consumption [ 28 ]. Pontes [ 31 ] had a similar finding in Portuguese middle school students, which suggested that the co-occurrence of problematic gaming and problematic social media use can lead to the deterioration of psychological health more so than either problematic behavior on its own. The studies also suggest that disordered gaming shares underlying risk factors (e.g., identity styles [ 34 ]) with problematic social media use and internet addition, suggesting that co-occurring problematic behaviors may share common identity styles, which act as risk factors in the co-occurrence of problematic online behaviors (i.e., gaming, social media, and internet use).

These results were consistent with the wider literature in regard to the association with potentially addictive behaviors and/or substance use [ 131 , 132 ], while the findings concerning disordered gaming also showed parallels with other behavioral addictions and substance disorder fields (e.g., gambling [ 133 , 134 ]). However, the variation in the consumption of substances or frequency of behaviors within the surveyed literature may indicate that traditional approaches in psychiatric comorbidities [ 135 ] and problem behavior theory [ 13 ] may not be a viable approach when assessing disordered consumption of substances and resulting behaviors. Gamers may instead be making pragmatic choices involving their consumption of substances, which may not be an indication of uncontrolled behavior [ 86 ]. For example, having increased amounts of caffeine or using “smart” drugs could be used to provide a competitive edge while gaming, which could be particularly true for those who play games professionally [ 136 ]. This may explain why illicit substance use (as opposed to legal substance use) varies in the surveyed literature, because it may be a choice by gamers to prolong their gaming with stimulants such as caffeine or nicotine [ 83 ]. However, gamers may choose to consume substances irrespective of videogame participation [ 86 ], which would explain the high rate of nicotine use [ 84 ] and alcohol use [ 130 ] in some samples of gamers. For example, if an individual is trying to quit smoking, they may increase their alcohol consumption (which has been associated with disordered gaming [ 30 ]). In an attempt to offset their need for nicotine, they may engage in other potentially addictive behaviors (e.g., alcohol consumption), which may then co-occur with an addiction, such as GD. This suggests an underlying association with disordered substance use, which can be seen in other disordered behaviors, such as gambling disorder [ 137 , 138 ].

Based on the empirical studies reviewed, problematic gamers consume a variety of substances while engaged in videogames. More specifically, while gaming, between 23.3–74.2% of gamers consumed caffeine [ 82 , 86 ], 21.7–25.3% smoked cigarettes [ 82 , 86 ], 41.2–44.6% smoked cannabis [ 28 , 82 ], 21.3–40.4% consumed alcohol [ 82 , 86 ], and 14.5% consumed illicit substances [ 86 ]. In regard to problematic and disordered behavior, the findings suggested that problematic gambling [ 85 ], problematic shopping, problematic sex, and problematic work [ 84 ] were associated with disordered gaming, while disordered exercise was not related [ 29 , 83 ].

Indeed, the presented evidence suggests that the co-occurrence of potentially addictive behaviors is not uncommon and is associated with a number of maladaptive outcomes for both adults [ 27 ] and adolescents [ 31 , 139 ]. There appears to be a clear divide between the experience of co-occurrence among adults and adolescents. The literature demonstrates that adults with disordered gaming frequently feature co-occurring problematic or disordered substance use (e.g., alcohol use [ 30 , 32 , 35 ]), while disordered eating [ 29 ] appears less frequently. However, the opposite appears to be true for adolescents, who appear to experience co-occurring disordered behaviors, such as social media addiction or problematic internet use [ 10 , 31 ]. The discrepancy between adults and adolescents may be explained due to the scarcity of available substances due to age-related factors [ 140 ] because disordered substance use is seen to increase as adolescents get older [ 102 ], allowing them to purchase alcohol or nicotine legally.

It is also worth noting that many of the problematic behaviors co-occurring with disordered gaming are ones that can be performed concurrently with gaming. For example, the surveyed literature shows that problematic exercise and problematic gaming co-occur. This may be attributed to the fact that gaming does not typically facilitate exercise, as gaming is largely a sedentary behavior, whereas exercise requires vigorous physical activity [ 29 ], which acts as a protective factor in GD [ 141 ]. This idea is also corroborated by the way the literature consistently shows that smoking and alcohol use co-occur with GD [ 27 , 30 , 32 , 130 ]. This may arise because the gaming context can facilitate the concurrent use of alcohol and smoking (i.e., nicotine or cannabis), especially if used as part of a coping strategy [ 30 ].

Coping strategies were one of the three ways (i.e., (i) coping strategies, (ii) emotional regulation and attachment, and (iii) personality characteristics) in which the development and maintenance of co-occurrence was considered in behavioral and substance addictions. Schneider et al. [ 102 ] considered coping strategies to be a key element in the development and maintenance of co-occurrence in an adolescent sample. Their results suggested that behavioral disengagement was a common coping strategy by those who experienced disordered gaming. One proposed reason of this resulting behavior is the self-medication hypothesis. This hypothesis suggests that in addiction-related disorders, individuals use substances in order to overcome painful affective states as well as related mental disorders [ 142 ], and this has been a common area of interest in problematic internet use [ 143 , 144 ]. It may also indicate that maladaptive coping strategies (i.e., emotional avoidance and/or behavioral disengagement) may play a key role in the development of co-occurring behaviors within disordered gaming. Furthermore, when these coping strategies co-occur, it is evident that these strategies will exacerbate disordered gaming symptoms, more so than either one on their own [ 145 ]. However, while there has been some literature to suggest that maladaptive coping strategies play an important role in problematic internet use [ 8 , 145 ], further research is needed in the case of disordered gaming.

Estévez et al. [ 7 ] suggested that disordered behavior (e.g., disordered gaming) and substance use may be explained utilizing emotional regulation and attachment theory. It has been suggested that low levels of emotional regulation are associated with an increase in risky behaviors, such as GD [ 146 ] and substance use [ 147 ]. Furthermore, emotional regulation is also predictive of addictive behaviors (but not substance addiction), suggesting that individuals with difficulty in emotional regulation may engage in addictive behaviors such as gaming to avoid (i.e., behavioral disengage) or regulate negative feelings or emotions (i.e., the self-medication hypothesis [ 7 , 145 , 148 ]). Moreover, Estévez and colleagues’ research suggests that attachment may also predict co-occurring use, specifically in behaviors that are potentially addictive. Poor peer-attachment was found to predict GD and gambling disorder, and poor maternal attachment predicted problematic internet use. Individuals with a secure attachment are characterized by a self-acceptance of emotional needs. However, an individual with a non-secure attachment style may pay little attention to their emotional needs and feel they have a lack of support [ 7 ]. This may then cause them to avoid interpersonal relationships [ 149 ], lending support to the notion that behavioral addictions may be understood as a form of escape and compensation for poor relationships [ 150 ]. Indeed, it could be suggested that individuals employ maladaptive behavioral coping strategies in response to poor emotional regulation or attachment, which may in turn aid in the development and maintenance of co-occurring at-risk behaviors.

Another dimension that has been considered in the development and maintenance of co-occurrence in GD is personality traits and factors. Low conscientiousness has been found to be associated with behavioral addictions (e.g., SNS addiction and GD [ 33 ]). This suggests that people who experience problematic or disordered gaming may have low conscientiousness and may have a low priority of duties and obligations [ 151 ], lack of planning ability [ 152 ], low self-control, weakness for temptations [ 153 ], and experience procrastination [ 154 ]. This is in line with Walther et al. [ 103 ], whose results suggested that individuals that experience problematic or disordered gaming also have high impulsiveness (i.e., a lack of self-control), which has been associated with problematic or disordered behavior, and/or substance use [ 155 ]. Furthermore, problematic gamers only shared a small overlap in personality factors with problem gambling (i.e., problematic behavior), even though problematic gambling shares more of an overlap in personality factors with problematic substance use than problematic gaming. However, problematic gamers reported higher scores on ADHD symptoms, high irritability/aggression, high social anxiety, and low self-esteem than any other addiction in Walther et al.’s paper [ 103 ], suggesting that gaming may take a unique dispositional position within the examined addictive behaviors here. The aforementioned studies indicate that personality traits or factors may impact the likelihood for co-occurrence to manifest in people experiencing problematic or disordered gaming.

The literature reviewed represents important examples of the next logical step in the progression of research beyond prevalence rates of co-occurrence. Each of the reviewed studies explored either specific psychological, sociological, and/or physiological factors. This in turn can guide future research into presenting a holistic representation of the specific risk factors (e.g., coping strategies and identity styles), which may contribute to developing, maintaining, or exacerbating co-occurring potentially addictive disorders. Furthermore, future research could help inform public policy and guide the development of treatment that encompasses the full clinical presentations of patients. However, only four recent studies [ 7 , 80 , 102 , 103 ] have taken the extra step to investigate the etiology and mechanisms of co-occurring disorders.

Understanding these processes is needed to further the understanding of addictive disorders. Nevertheless, the extant findings are beneficial in advancing the field and providing a framework for how to consider the mechanisms of co-occurring addictive behaviors in a multifaceted manner. Furthermore, the present review also highlights the potential for differing mechanisms of action, despite similar observed effects, suggesting that behavioral and substance addictions, and their co-occurrence involve complex processes. In understanding these factors, treatment efficacy may be increased by targeting common etiological mechanisms across multiple disorders (e.g., coping mechanisms [ 102 ], or personality factors [ 103 ]), much like the direction of the literature within the substance disorders field.

Co-occurrence Within Disordered Gaming Compared to the Substance Disorder Literature

Arguably, GD is one of the newer behavioral disorders to be investigated. Nevertheless, past substance use disorder literature can be used to provide a reference point on how to advance the co-occurrence research into disordered gaming. The drug and alcohol abuse literature appears to focus on the epidemiology of co-occurrence as it appears to be commonly studied [ 156 ], a trend that the GD literature is following. Furthermore, within the substance abuse literature, co-occurring behavioral and substance addictions appear to be commonly considered in both the general and clinical populations [ 25 , 87 ], indicating that the GD literature should also mimic this global approach. In addition, individuals with co-occurring behavioral or substance use disorders (or problematic use) tend to have poorer functioning and treatment outcomes, much like individuals with disordered gaming [ 156 , 157 , 158 ]. These findings within the substance use literature are in part facilitated by the longitudinal research investigating the development, maintenance, and remission of each disorder, which the present field of co-occurrence in disordered gaming lacks.

While research on GD focuses on the prevalence and co-occurrence of psychiatric disorders [ 23 ], the substance use literature has gone much further by investigating and identifying the epidemiological factors of co-occurrence and the impact co-occurring disorders can have. For example, there have been a number of studies that have investigated a wide range of underlying mechanisms between co-occurring substance use and other disorders such as neurobiological commonalities, genetic markers, temporal changes, and qualitative research focusing on behavioral changes [ 159 , 160 ]. Furthermore, the substance use literature has also investigated whether treating one disorder causes the accompanying co-occurring disorder to go into remission, concluding that it can vary depending upon the disorders and individual presentation [ 25 , 161 ]. However, when looking to research concerning disordered gaming, this additional step has not yet been made, and the effects of co-occurrence and its impact on course of illness and by type of disorder are not yet known. Additionally, the substance abuse literature has also closely examined the exacerbating effects of multiple co-occurring disorders [ 158 , 162 ]. While the research on disordered gaming has begun to move in this direction, research on substance use has attempted to separate various dimensions of co-occurrence (e.g., psychiatric disorders, mental health, and social functioning) by controlling for their effects on the primary disorder in question [ 163 ].

Finally, when considering treatments, the substance use literature has paved the way for behavioral disorders. There is a general agreement that co-occurring disorders may require an integrated approach [ 164 , 165 ] which consider not just the primary disorder, but also the co-occurring disorder. For example, in a systematic review on people who experiences severe mental illness and co-occurring substance use suggested that motivational interviewing in conjunction with cognitive behavior therapy (CBT; targeting both substance use, and mental health respectively) showed “quality” evidence for reducing substance use and improving mental health than just CBT alone. However, this type of approach is not near the level of acceptance as more traditional treatments (such as CBT), although there are considerable efforts to evaluate its efficacy in the substance use field [ 24 , 162 ]. In contrast, the research into integrative treatments that targets both disordered gaming and co-occurring addictive disorders is, to the best of authors’ knowledge, notably absent from the literature.

Future Research

A majority of the surveyed literature does not go beyond measures of association and with measures of prevalence being questionable due to overwhelming lack of representativeness of samples. The published literature suggests that there are various behavioral and substance-related addiction disorders that have the potential to co-occur with GD. However, there is very little additional literature that continues to investigate this further. Regarding the co-occurrence of disordered gaming with other behavioral addictions, only a few studies exist, suggesting a co-occurrence with problematic gambling, shopping, and social media use. While there has not been an extensive amount of literature on the co-occurrence prevalence rates of disordered gaming with other addictive behaviors, it has been explored across several geographical locations and cultures, indicating that it is moving in a similar direction of other addiction-related literature (e.g., gambling [ 157 ]). However, while it is important that this line of enquiry is followed, it is also important to investigate the etiological aspects of co-occurrence within GD because it is experienced differently across culturally diverse groups of people.

There is a significant gap in the literature when it comes to longitudinal studies that focus on the changes of co-occurring addictive disorders over time. The current literature establishes that co-occurrences between disordered gaming and other addictive-related disorders are common. Furthermore, no paper to the authors’ knowledge has investigated whether disordered gaming preceded the onset of another co-occurring addictive disorder or vice versa. It is imperative to understand how co-occurring disorders interact over time in order to develop appropriate treatment methods. Moreover, models for hypothesizing potential treatment frameworks and outcomes, which consider onset or remission of other co-occurring problematic or disordered behavior, would be instrumental in improving potential effectiveness of treatment methods. For example, having confidence that disordered gaming symptoms typically occur within specific substance abuse disorders (e.g., alcohol or cannabis abuse) may allow for a more tailored approach that targets both disordered gaming and the co-occurring use of other behaviors or substances. Future studies should also consider investigating the time of onset in relation to disordered gaming because this would also provide more robust data and allow for more significant conclusions to be drawn.

Substance Use Literature May Act as a Model to Guide Future Research

A finding that was consistent across both adults and adolescents was that those who presented with problematic or disordered gaming and a co-occurring addiction-related condition consistently reported more severe experiences as assessed using clinical measures [ 27 , 30 , 31 ], which is mirrored within the substance abuse literature [ 25 , 166 ]. Another way in which the reviewed literature mirrored the substance use literature is the calls for the early intervention for individuals experiencing co-occurring disorders [ 167 ], with a number of studies calling for additional early intervention screening measures [ 30 ], providing psychoeducation on the co-occurring disorder [ 27 ], or considering shared clinical features (e.g., personality factors [ 33 ]). These suggestions highlight the need for careful clinical assessment of co-occurring problematic behaviors that may have developed on a subclinical level and, thus, might contribute to the primary disorder.

The momentum of research examining GD more generally has increased and those in the field are engaging in effective efforts to understand the impact of co-occurring addictive behaviors. The substance use literature provides various research frameworks and designs that could be utilized in the future to bring gaming research in line with the wider field of addictive disorders. For example, investigating the nuances between different co-occurring disordered use in clinical samples [ 25 ], continuing investigations into prevalence, but expanding and evaluating the epidemiological data of such impacts as onset and remission [ 22 ], and establishing clinical trials and protocols that are tailored toward individuals presenting with co-occurring disorders [ 25 , 160 ].

Limitations

Although the present review identified several important trends within the disordered gaming co-occurrence literature, it is subject to limitations. Firstly, methodology used in the review was descriptive and does not quantitatively synthesize data. Although the authors followed a rigorous and transparent review methodology, it still investigates the breadth of literature, rather than its depth, and as such, no statistical conclusions can be drawn from the results. Secondly, the study excluded literature that was not peer-reviewed. Furthermore, the inclusion criteria meant that only English language papers were reviewed, limited by a specific set of databases and search terms. As a result, the authors may have missed relevant studies in other languages or databases. As with any review, screening and selection is always a subjective process and is thus prone to biases. Despite capturing a wide range of research terms in several databases, it is possible that relevant studies may have been missed due to a lack of fit with the inclusion criteria. Finally, considering only the use of papers that were published in the last several years may have also contributed the small amount of papers on co-occurrence and gaming disorder.

The evidence in the present review suggests an increase in research interest on co-occurrence of other addiction-related behaviors with disordered gaming. However, currently, most research investigates the prevalence rates of co-occurring addiction-related disorders with disordered gaming and frequently demonstrated the potential for co-occurrence between problematic and disordered behaviors and substance use. Various reviewed papers considered novel ways to investigate the potential development and maintenance of problematic and disordered gaming and its co-occurrence, which could be improved further by considering the frameworks and study designs used in the substance addiction disorder literature. Indeed, the research indicates that co-occurrence in problematic and/or disordered gaming is common, and when examining the substance use field as a guide, outcomes may be improved when separate treatment modalities for these co-occurring disorders are offered in combination. While it is not certain how well these treatment models may work in a diverse population, current research consistently calls for trials of multimodal treatment (i.e., using tailored treatments that consider co-occurring behavior or substance use) to take place.

Papers of particular interest, published recently, have been highlighted as: • Of importance

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Burleigh, T.L., Griffiths, M.D., Sumich, A. et al. A Systematic Review of the Co-occurrence of Gaming Disorder and Other Potentially Addictive Behaviors. Curr Addict Rep 6 , 383–401 (2019). https://doi.org/10.1007/s40429-019-00279-7

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Published : 07 September 2019

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