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Metacognition refers to the awareness of one’s own thoughts and behaviors, as well as the ability to monitor and alter behavior. It encompasses any cognitive process that receives information from and exerts a controlling influence on another cognitive process [-]. More specifically, it comprises metacognitive knowledge and metacognitive regulation. Metacognitive knowledge refers to information and beliefs about one’s cognitive processes, while metacognitive regulation pertains to skills to regulate thoughts, including planning, supervision, and regulation []. Metacognition contributes to effective decision-making across a variety of contexts []. For instance, it facilitates the smooth operation of ongoing thought and behavior by helping us recognize our errors [], regulate the deployment of executive function [], and detect lapses of attention []. Originating from cognitive psychology, metacognition has been linked to psychological disturbances [,].
In recent years, studies have highlighted the potential role of metacognitions in the development of addictive behaviors, such as problematic gaming behavior [,]. However, due to the lack of suitable research instruments, conducting further investigations in China has been challenging. To address this issue, this study aimed to evaluate the validity of the Metacognitions about Online Gaming Scale (MOGS) [] among the Chinese population and its association with gaming behavior.
According to the self-regulatory executive function model, metacognitions play a critical role in the occurrence and development of psychological dysfunction []. In this model, psychological dysfunction is activated and perpetuated by a fixed thinking pattern called cognitive attentional syndrome (CAS), which comprises several maladaptive coping strategies (eg, rumination, threat-monitoring, and avoidance). The CAS is driven and maintained by maladaptive metacognitions []. Maladaptive metacognitions mistakenly regard the CAS as an effective coping style, resulting in a vicious cycle of ineffective self-regulation []. Over the last 40 years, metacognitions have been associated with several mental and psychological problems [], such as obsessive-compulsive disorder, schizophrenia, addiction, anxiety, and depression [-].
In the domain of addictive behaviors, metacognitions are divided into 2 subtypes: positive and negative []. The former refers to the beliefs that engaging in specific addictive behaviors is a strategy of affective and cognitive self-regulation, such as “Drinking helps me think more clearly” and “Gambling can improve my mood” [,]. The latter refers to the concerns about the uncontrollability and danger of thoughts or engagement with addictive behaviors. For example, “Drinking will interfere with my thought” and “Once I start thinking about drinking, I cannot stop” []. Previous studies have shown that positive metacognitions can motivate addictive behaviors in the early stage, while negative metacognitions contribute to their perpetuation by activating negative emotional states as a reinforcement [,]. In recent years, metacognition has been correlated with many addictive behaviors, such as problematic alcohol use [-], nicotine dependence [,], gambling disorder [-], problematic Internet use [-], problematic social media use [-], and Internet Gaming Disorder (IGD; problematic online gaming) [,].
As an addictive behavior, IGD was first included in the research appendix section of the Fifth Edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) in 2013 [], then it was officially included in the addiction disease unit of the Eleventh Revision of the International Classification of Diseases (ICD-11) in 2018 []. Its core characteristics include losing control while gaming, prioritizing gaming over other interests, and causing functional damage in daily life. Excessive online gaming results in various problems, such as sacrificing real-life relationships, sleep, work, and education, leading to brain damage [-]. According to a recent review, the global prevalence of IGD was 3.05%, and it was higher among Asians (5.08%) than Europeans (2.72%) []. In China, the prevalence ranges from 3.5% to 17%, which is higher than the global average level [-].
In order to effectively prevent and treat this disorder, extensive research has been conducted to investigate its etiology. These studies have revealed a significant association between IGD and various psychological factors, including negative affect, gaming motives, and maladaptive cognition []. The Interaction of Person-Affect-Cognition-Execution (I-PACE) model proposed by Brand et al [,] suggests that the initiation of addictive behaviors arise from the integration of emotional and cognitive responses to internal or external stimuli along with specific motivations. Motives are sets of knowledge that represent the emotional preferences expressed in our thoughts and concepts. Gaming motives could be considered as stimulating factors of gaming behavior, which may play an important role in the development of IGD []. Furthermore, Spada et al [] posited that the development and persistence of addictive behaviors, including IGD, are strongly influenced by particular metacognitions about addictive behaviors.
According to previous studies, metacognitions have been associated with IGD [,]. However, these studies mainly focused on generic metacognitions (eg, beliefs about worry, cognitive monitoring, the need for thought suppression). To assess specific metacognitions about online gaming, Spada and Caselli [] developed a self-rating instrument called the Metacognitions about Online Gaming Scale (MOGS). In the original validation of the MOGS, an exploratory factor analysis (EFA) was performed with 225 adults in Study 1 that suggested a 2-factor solution: Negative Metacognitions about Online Gaming (N-MOG; 6 items) and Positive Metacognitions about Online Gaming (P-MOG; 6 items) []. The N-MOG assesses negative metacognitions about the uncontrollability and danger of thoughts on gaming. The P-MOG measures positive metacognitions in which online gaming helps individuals regulate affect and thought. In Study 2, the confirmatory factor analysis (CFA) with another sample of 348 individuals further divided N-MOG into 2 factors and built a 3-factor structure: Negative Metacognitions about the Uncontrollability of Online Gaming (N-MOGU), Negative Metacognitions about the Dangers of Online Gaming (N-MOGD), and P-MOG []. All 3 factors reported adequate internal reliability (Cronbach α≥.79). While exploring predictive validity, the study showed that MOGS was positively related to gaming hours and Internet addiction []. Overall, these findings demonstrated the reliability and validity of the MOGS.
To extend the utility of the MOGS to adolescent populations from other countries, Akbari et al [] translated it into Persian and evaluated its psychometric properties among 769 Iranian adolescents. The results showed that the 3-factor structure had appropriate construct validity and internal consistency (Cronbach α≥.79). Furthermore, metacognitions about online gaming were able to independently predict problematic gaming behavior while controlling for personality traits, gaming motives, gaming-related cognitions, and negative affect []. Another study investigating the association between IGD and social anxiety reported that metacognitions about online gaming were significantly correlated with IGD and mediated the latter’s relationship with social anxiety [].
These studies indicated an association between specific metacognitions about online gaming and IGD. Further exploration could be beneficial for the treatment and prevention of IGD, especially in countries with a higher prevalence, such as China. However, it is difficult to conduct relevant research in China because of the lack of instruments used to evaluate specific metacognitions. Therefore, the primary objective of this study was to translate the MOGS into Chinese and validate its psychometric properties among Chinese adolescent and adult gamers using online convenience sampling. Additionally, the study aimed to investigate the unique influence of metacognitions about online gaming on IGD while considering variables such as anxiety, depression, and motivation. The hypothesis was that, within the Chinese population, positive and negative metacognitions about online gaming would serve as independent risk factors for IGD, distinct from other contributing factors.
We recruited all individuals online through convenience sampling in June 2021. The inclusion criteria were as follows: (1) age ≥13 years, (2) Chinese speakers who could understand the questionnaires, (3) consent to participate (adolescents with parental consent), and (4) played games at least one hour every week (excluding online gambling) in the last 12 months.
In total, 996 individuals participated in this survey. We excluded 88 individuals whose answer was “No” to the item “Are your answers to this questionnaire true and reliable?”, 37 who gave the same answers to more than 50% of the questions and whose time spent on the questionnaire was less than the mean minus 3 SD, and 99 who were younger than 13 years old. The final sample included 772 participants.
Ethical ConsiderationsBefore starting the anonymous online investigation, participants were informed about the purpose and rights of the study and signed an online informed consent form. Those younger than 18 years needed to inform their guardians and obtain consent before filling out the questionnaire. The ethics committee of the Second Xiangya Hospital of Central South University approved this study (protocol code 2020004; dated March 1, 2020).
MeasuresBasic InformationBasic information included sociodemographic information and Internet gaming characteristics. The former included gender, age, employment, years of education, and family structure (eg, single-child family). For the latter, participants reported their average time spent gaming (weekly gaming hours), gaming devices (a multiple-choice question), the number of long-term gaming partners, and self-evaluation of gaming addiction.
Metacognitions About Online GamingMetacognitions about online gaming were measured using the MOGS [], which contains 12 items rated on a 4-point Likert scale (1=Do not agree to 4=Agree very much). The MOGS comprises the following 3 factors: (1) N-MOGU (3 items, such as “Once I start online gaming I cannot stop”), (2) N-MOGD (3 items, such as “Online gaming makes me lose control”), and (3) P-MOG (6 items, such as “Online gaming stops me from worrying”). A higher score indicates a higher degree of specific metacognition about online gaming.
IGD SymptomsThe severity of IGD symptoms was assessed using the Internet Gaming Disorder Scale-Short Form (IGDS9-SF) [,]. The IGDS9-SF is a 9-item scale developed from the core symptoms of IGD proposed by the DSM-5 and assesses gaming activities and their adverse effects in the past 12 months. All items are rated on a 5-point Likert scale (1=never to 5=very often). The scores range from 9 to 45. Higher scores represent more severe IGD symptoms. With adequate reliability (Cronbach α≥.9), the Chinese version of the IGDS9-SF was used in our research [,]. The Cronbach α was .90 in this study.
Gaming MotivesWe assessed gaming motives using the Motives for Online Gaming Questionnaire (MOGQ) []. It includes 27 items comprising the following 7 motivational dimensions (all rated on a 4-point Likert scale): escape, skill development, recreation, competition, coping, fantasy, and social. The Chinese version of the MOGQ has excellent reliability (Cronbach α≥.83) and validity []. Higher scores reflect stronger motives for online gaming. In this study, the Cronbach α was .95 for the total scale and ranged from 0.84 to 0.89 for each subscale.
Depression and AnxietyThe Patient Health Questionnaire-9 (PHQ-9) [] was used to measure depressive symptoms. It is a diagnostic screening tool that monitors the severity of depression over the last 2 weeks. All items are scored on a 4-point Likert scale (0=Not at all to 3=Nearly every day). The scores range from 0 to 27. Higher scores denote worse depressive symptoms. The Chinese version of the PHQ-9 [] has suitable reliability (Cronbach α=.85). The Cronbach α was .89 for this study.
The Generalized Anxiety Disorder-7 (GAD-7) [] was used to measure anxiety symptoms. It is a self-rated scale that assesses the severity of anxiety symptoms over the last 2 weeks. All items are scored on a 4-point Likert scale (0=Not at all to 3=Nearly every day). The scores range from 0 to 21. Higher scores represent worse anxiety symptoms. The Chinese version of the GAD-7 [] was used, with appropriate internal consistency (Cronbach α=.90) and validity. The Cronbach α was .92 for this study.
ProceduresThe MOGS was translated into Chinese by 2 professional translators using a standard translation and back-translation method []. For some controversial items (eg, “Online gaming makes me lose control,” “Online gaming makes my worries more bearable”), we consulted the author of the original scale. Considering the original scale, 2 bilingual psychologists revised the translated version and checked its face validity. A pilot study was conducted with 5 adults and 5 adolescents to test the understandability. Based on their feedback, some descriptions of items were modified, and the final Chinese version of the MOGS (C-MOGS) was created.
We conducted this survey online using Questionnaire Star, a professional online survey platform. By reading recruitment advertisements posted on social networking sites (eg, WeChat, Weibo, and other webcast platforms), individuals could open the questionnaire link. On the first page, participants could read the objectives and content of this research and confirm their participation (minors, those younger than 18 years, had to obtain the consent of their guardian). Each IP address can only be used once to avoid repeated participation. After the questionnaire was submitted, all the data were sent to the researcher's account, and only the researcher could view the data.
Data AnalysesData analyses were conducted using SPSS version 25.0 (IBM Corp) and AMOS version 24.0 (IBM Corp). First, basic statistical analyses (eg, descriptive analysis, independent samples t tests, chi-square tests) were performed on sociodemographic variables and Internet gaming characteristics. To analyze the construct of the C-MOGS, the total sample was randomly split into 2 subsamples. Sample-1 (n1=390; 229/390, 58.7% male; age: mean 22.25, SD 9.05 years) was used for EFA, and sample-2 (n2=382; 229/382, 59.9% male; age: mean 21.14, SD 8.52 years) was used for CFA. Except for EFA and CFA, all analyses were conducted on data from the entire sample. Independent samples t tests and chi-square tests showed that there were no significant differences between the 2 subsamples regarding age (t770=–1.754, P=.08), gender (χ21=0.121, P=.73), average weekly gaming time (t770=0.775, P=.44), gaming devices (χ21<1.012, P=.31), long-term gaming partner (χ23=4.046, P=.26), and other demographic variables (P=.33-.88). An EFA with principal component analysis (PCA) and varimax-rotation method was conducted on the C-MOGS items. To validate the models derived from the EFA, a CFA was completed with AMOS 24.0 using the maximum likelihood method. The model fit was appraised using multiple fit indexes, including chi-squared:degree of freedom ratio (χ2/df<5), goodness-of-fit index (GFI>0.90), Tucker-Lewis Index (TLI>0.90), comparative fit index (CFI>0.90), standardized root of the mean square residual (SRMR<0.08), root mean square error of approximation (<0.05=close fit; <0.08=acceptable fit; <0.1=mediocre fit) []. The reliability of the C-MOGS was examined by assessing the internal consistency of the scale and subscale. Acceptable values for the Cronbach α and Guttman split-half coefficients are >0.70, while values >0.80 are considered good []. Finally, to test the concurrent and incremental validity, correlation analyses and hierarchical multiple regression analyses were conducted between the C-MOGS and Internet gaming characteristics (gaming time, IGD, gaming motives) as well as anxiety and depression.
In this study, analysis was conducted on data from 772 participants (458 men, 59.3%) aged between 13 years and 57 years (age: mean 21.70, SD 8.81 years; participants aged 13-17 years: 281/772, 36.4%). The majority of them were students (555/772, 71.9%). Smartphones were the most popular device for gaming (705/772, 91.3%). Of the sample, 69.4% (536/772) had one or more long-term gaming partners. Participants spent an average of 13.43 (SD 10.88) hours every week playing games. More details on the sample characteristics are shown in .
Table 1. Sociodemographic and Internet gaming characteristics of the sample (n=772).CharacteristicParticipants’ resultsGender, n (%)To identify the potential factorial structure of the C-MOGS, an EFA was performed on data from sample-1 (n=390). First, we used the Kaiser-Meyer-Olkin (KMO) and Bartlett tests of sphericity to ensure that the sample was suitable for EFA. The KMO value was 0.894, and the Bartlett test of sphericity was significant (χ266=3159.742, P<.001), confirming the data were sufficient.
The initial analysis extracted 2 factors using the criteria of an eigenvalue>1 and factor loading>0.40. The 2-factor solution (eigenvalues of 5.573 and 2.597) accounted for 68.08% of the total variance, and the loading of all the items was >0.4 (0.646-0.918; ). Factor 1 included items 1 through 6, referred to as the N-MOG; factor 2 included items 7 through 12, which described the P-MOG.
Additionally, according to the dimension of the original scale [], we also conducted a PCA by setting 3 factors to be extracted. The 3-factor solution (eigenvalues of 5.573, 2.597, and 0.797) explained 74.73% of the total variance (37.44%, 19.31%, and 17.98%, respectively). Item-factor loadings are presented in . The factors were as follows: factor 1 (items 1, 2, and 3) referred to the N-MOGU; factor 2 (items 4, 5, and 6) was related to the N-MOGD; and factor 3 (items 7, 8, 9, 10, 11, and 12) was related to the P-MOG [].
Table 2. Item-factor loadings of the Chinese version of the Metacognitions about Online Gaming Scale (C-MOGS) based on exploratory factor analyses (sample-1, n=390).Items2-factor model3-factor modelF1a,bF2b,cCommunalityF1b,dF2b,eF3b,cCommunality(1) I continue to play despite I think it would be better to stop0.6460.2150.4630.8090.1100.1780.698(2) I have no control over how much time I play0.8300.1770.7210.8080.3700.1510.813(3) Once I start online gaming, I cannot stop0.8080.1780.6840.7430.4030.1570.739(4) Online gaming makes me lose control0.7460.1080.5690.2130.8400.1340.768(5) Thoughts about online gaming interfere with my functioning0.7430.0070.5520.2220.8250.0320.731(6) Thoughts about online gaming are becoming an obsession0.7760.1650.6300.4740.6240.1690.643(7) Online gaming makes my worries more bearable0.2750.7340.6140.1900.2040.7350.618(8) Online gaming reduces my negative feelings0.1310.8820.7960.0870.1040.8850.801(9) Online gaming helps me to control my negative thoughts0.0930.9180.8520.1250.0140.9150.852(10) Online gaming stops me from worrying0.1160.8490.7340.0830.0870.8500.737(11) Online gaming reduces my anxious feelings0.1070.9170.8530.0810.0760.9190.856(12) Online gaming distracts my mind from problems0.2220.8080.7030.2560.0640.8000.710aNegative Metacognitions about Online Gaming (N-MOG).
bFactor loadings present the factor matrix values.
cPositive Metacognitions about Online Gaming (P-MOG).
dNegative Metacognitions about the Uncontrollability of Online Gaming (N-MOGU).
eNegative Metacognitions about the Dangers of Online Gaming (N-MOGD).
Confirmatory Factor AnalysisTo further evaluate the structural validity of the C-MOGS, we conducted a CFA on sample-2 (n=382) using AMOS 25.0. We compared the goodness of model fit between the 2 aforementioned models. We first tested the 2-factor model, which had a substandard fit in some indexes: χ2/df=3.962 and root mean squared error of approximation (RMSEA)=0.880. In comparison, the 3-factor model showed an adequate model fit: χ2/df=3.477, GFI=0.929, CFI=0.958, TLI=0.945, SRMR=0.065, RMSEA=0.081 (). The correlations between P-MOG, N-MOGU, and N-MOGD were moderate (r=0.389 and 0.377, respectively) and were relatively strong between N-MOGU and N-MOGD (r=0.905).
Due to the high correlation between the 2 negative metacognitive factors, we also created a bifactor model (), in which N-MOGU and N-MOGD loaded on a second-order factor (N-MOG) and P-MOG was a first-order factor. In this model, the goodness of model fit was the same as that of the 3-factor model (), and the correlation between N-MOG and P-MOG was moderate (r=0.396).
Table 3. Model fit indices of the confirmatory factor analyses for the Chinese version of the Metacognitions about Online Gaming Scale (C-MOGS; Sample 2, n=382).Modelχ2 (df)χ2/dfGFIaCFIbTLIcSRMRdRMSEAe2-factor model205.517 (52)3.9620.9170.9480.9350.0670.8803-factor model173.867 (50)3.4770.9290.9580.9450.0650.810Bifactor model173.867 (50)3.4770.9290.9580.9450.0650.810aGFI: goodness-of-fit index.
bCFI: comparative fit index.
cTLI: Tucker-Lewis Index.
dSRMR: standardized root of the mean square residual.
eRMSEA: root mean square error of approximation.
Figure 1. The bifactor model of the Chinese version of the Metacognitions about Online Gaming Scale (C-MOGS), showing the latent factors as ovals, the 12 items in the C-MOGS as rectangles, the error terms (e1-e14) as circles, and the standardized factor loading above the arrows. N-MOG: Negative Metacognitions about Online Gaming; N-MOGD: Negative Metacognitions about the Dangers of Online Gaming; N-MOGU: Negative Metacognitions about the Uncontrollability of Online Gaming; P-MOG: Positive Metacognitions about Online Gaming. ReliabilityThe Cronbach α coefficient and split-half reliability were calculated for the scale and its subscales in the total sample (n=772). The α coefficient for the total scale was .894, and it was .823 for the N-MOGU, .799 for the N-MOGD, and .925 for the P-MOG. No item deletion improved the internal consistency. The Guttman split-half coefficient of the overall scale was 0.942, and for each dimension, the coefficients were 0.776, 0.754, and 0.841. These findings confirmed that the C-MOGS and its subscales exhibit adequate internal consistency.
Moreover, we calculated the correlation coefficient between each item and its relative factor scores. The results showed that the item-total correlations for all items were high (r≥0.551).
Concurrent ValidityWe further analyzed the correlation between the 3 factors of the C-MOGS and IGD, gaming motives, anxiety, and depression to test the concurrent validity. Based on the Shapiro-Wilk test, these variables did not follow a normal distribution (all Ps<.05). Therefore, Spearman correlation analysis was chosen to explore the relationships between the variables. shows the descriptive statistics (median and IQR), and shows the correlations between the variables. Each factor of the C-MOGS showed positive correlations with the IGDS9-SF, weekly gaming hours, every dimension of the MOGQ, the PHQ-9, and the GAD-7 (r=0.153 to 0.759, all Ps<.01). Moreover, the correlation matrix showed positive correlations between the IGDS9-SF and the other variables (r=0.352 to 0.700, all Ps<.01).
Table 4. Descriptive statistics for the variables (n=772).VariablesMedian (IQR)RangeIGDS9-SFa17 (10)9-45WGHb9 (11)1-69Motives for Online Gaming Questionnaire (MOGQ)aIGDS9-SF: Internet Gaming Disorder Scale-Short Form.
bWGH: weekly gaming hours (average time).
cPHQ-9: Patient Health Questionnaire-9.
dGAD-7: Generalized Anxiety Disorder-7.
eN-MOGU: Metacognitions about the Uncontrollability of Online Gaming.
fN-MOGD: Negative Metacognitions about the Dangers of Online Gaming.
gP-MOG: Positive Metacognitions about Online Gaming.
Table 5. Spearman correlation coefficients among the variables (n=772).VariablesIGDS9-SFaWGHbMOGQcPHQ-9dGAD-7eN-MOGUfN-MOGDgP-MOGhaIGDS9-SF: Internet Gaming Disorder Scale-Short Form.
bWGH: weekly gaming hours (average time).
cMotives for Online Gaming Questionnaire.
dPHQ-9: Patient Health Questionnaire-9.
eGAD-7: Generalized Anxiety Disorder-7.
fN-MOGU: Metacognitions about the Uncontrollability of Online Gaming.
gN-MOGD: Negative Metacognitions about the Dangers of Online Gaming.
hP-MOG: Positive Metacognitions about Online Gaming.
iNot applicable.
Incremental ValidityWe conducted a hierarchical multiple linear regression analysis to identify the incremental effect of metacognitions about online gaming on IGD. The IGDS9-SF was the outcome variable, and the 3 factors of the C-MOGS were predictor variables, along with other variables related to the IGDS9-SF (gender, weekly gaming hours, the 7 factors of the MOGQ, and the total PHQ-9 and GAD-7 scores). Each variable was input in the following order: step 1: age and gender (0=female, 1=male); step 2: weekly gaming hours; step 3: the 7 factors of the MOGQ; step 4: GAD-7, PHQ-9; step 5: the 3 dimensions of the C-MOGS.
The Durbin-Watson statistic showed that the observed values were independent of each other (D-W=2.077). All tolerance values were above 0.1 (0.180-0.878), indicating no multicollinearity. The results are presented in . The 3 factors of the C-MOGS accounted for 13.0% of the variance in the IGDS9-SF (P<.001). In step 5, the final model indicated that gender, weekly gaming hours, the PHQ-9 score, the MOGQ-Escape score, the MOGQ-Competition score, and the factors of the C-MOGS were significant positive predictors of the IGDS9-SF (R2=0.729, P<.001, adjusted R2=0.724), and the most important predictor was the N-MOGU (β=0.326, P<.001).
Table 6. Hierarchical multiple regression analyses with the Internet Gaming Disorder Scale-Short Form (IGDS9-SF) as the outcome variable and the Chinese version of the Metacognitions about Online Gaming Scale (C-MOGS) factors as predictor variables, together with gender, weekly gaming hours, motives related to online gaming, depression, and anxiety (n=772).
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