Special Collection: Behavioral Addiction to Technology. Volume 4, Issue 3. DOI: 10.1037/tmb0000112
International prevalence rates for gaming disorder range with approximately 3.05% of individuals meeting criteria. Despite the high potential for diagnosis, most clinicians in health care facilities who treat known comorbidities (e.g., anxiety or depression) do not assess clients at intake for gaming disorder. The present study aims to evaluate the Brief Internet Gaming Screen–8 (BIGS-8) as a self-assessment screening tool within a health care setting treating clients with comorbid disorders. The measure was administered to individuals in a U.S. treatment facility that specializes in treating gaming disorder and technology overuse (n = 128). The participant’s ages were 13–35. The majority (87.9%) of individual’s primary presenting behavior for which they sought treatment was due to impairment in psychosocial functioning associated with video gaming. To discover the factor structure of the BIGS-8, a parallel analysis scree plot and an exploratory factor analysis were conducted using half of the sample chosen at random (n = 64). A confirmatory factor analysis was conducted on the other randomly chosen half of participants (n = 64). Results indicated a one-factor solution. To explore convergent validity, the sum score of the BIGS-8 was significantly positively correlated with the Depression Anxiety Stress Scale–21 (DASS-21) Depression subscale and DASS-21 Anxiety subscale sum scores. Within a components-based addiction framework aligned with the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition–Text Revision criteria, the BIGS-8 yielded an acceptable model fit. The BIGS-8 poses clinical utility of identifying behavioral addiction elements that align with common comorbidities within a clinical sample and may be useful as a preliminary screening tool prior to completing a more comprehensive clinical assessment.
Keywords: gaming disorder, problematic gaming, assessment, screening
Disclosures: This article was a collaborative effort among all authors and the authors state that they do not have any interests that could constitute a real, potential, or apparent conflicts of interest and confirm no financial or other relations with companies, trade associations, unions, or groups (including civic associations and public interest groups) that may gain or lose financially from the results or conclusions in the study. The authors have no known conflicts of interest to disclose.
Data Availability: Data collection comprised of four independent researchers adhering to Health Insurance Portability and Accountability Act federal mandates to collect and develop a database from clinician input gathering in an electronic medical records. Due to privacy laws regarding personal health records information in the United States, the data set is not publicly available. Researchers interested in examining the data set can contact Hilarie Cash or Cosette Rae for clearance procedures.
Correspondence concerning this article should be addressed to Stephanie L. Diez, Department of Social Work, Sociology, and Human Services, College of Health Sciences and Human Services, Pennsylvania Western University, Hendricks Hall G48, 325 Scotland Road, Edinboro, PA 16444, United States. Email: [email protected]
Internet gaming continues to grow in popularity as one of the most popular leisure activities with approximately 227 million video game players in the U.S. alone (Entertainment Software Association [ESA], 2021). The frequency and duration of time spent engaging in video game playing have increased substantially over the past years with about half, 51%, of U.S. video game players spending an average of over 7 hr per week playing video games (ESA, 2021). Although the majority of individuals who engage in video game playing do so as a form of recreation, the potential for this behavior to be linked with adverse biopsychosocial implications has been the subject of increased research across the world (Chen et al., 2020; Kim et al., 2019). Most notably, interpersonal conflict and adverse consequences are often the first signs that video gaming behavior has progressed from leisure to problematic or disordered gaming (King et al., 2019; Mihara & Higuchi, 2017). Evidence has indicated an international prevalence rate of 3.05% for individuals with signs and symptoms of pathological gaming disorder (GD; Stevens et al., 2021). In comparison, international prevalence rates for substance use disorders have been reported at 2.6% (Degenhardt et al., 2017). Despite the aforementioned prevalence rates, the majority of clinicians and health care facilities do not assess patients at intake for gaming disorder as they do with anxiety or depression which in part may be due to exclusion of a self-administered screening tool for gaming disorder (Peter et al., 2020). Therefore, the present study’s aim is to evaluate a self-assessment tool with potential clinical utilization observed within a health care setting.
In 2013, the American Psychiatric Association (APA) included diagnostic criteria for internet gaming disorder (IGD) in the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5) and the DSM–5–Text Revision (DSM-5-TR) under Results section, stating further research is necessary to solidify the proposed criteria (American Psychiatric Association [APA], 2013; APA, 2022). Subsequently, in 2018, the World Health Organization (WHO) included GD in the International Classification of Diseases 11th Edition (ICD-11) as a formal disorder (World Health Organization [WHO], 2018). Both the APA and WHO define GD as a behavioral pattern of video gaming behavior characterized by psychosocial impairments and loss of control where the continuation or escalation of gaming intensifies despite the occurrence of adverse consequences (APA, 2013, 2022; WHO, 2018). Research indicates some of the adverse impacts from GD include: increased mental health symptoms of anxiety, distress, depression, sleep problems, reduced functional performance at school and/or work, problems with interpersonal relationships (e.g., with family, friends, significant others), and feelings of reduced self-esteem and self-worth (Kuss et al., 2020; Sussman, 2020).
GD is prevalent in many countries (Petry et al., 2018) and since being presented as a diagnostic consideration warranting further study in 2013, substantial research has supported inclusion of the criteria (Peter et al., 2020; Pontes & Griffiths, 2015; Stevens et al., 2021). Various assessments for GD have been developed and validated according to the WHO and APA criteria (Chen et al., 2020; King et al., 2020; Pontes et al., 2020) utilizing varying international samples and thus their clinical utility varies. Furthermore, individuals who meet pathological criteria for GD often coexperience symptoms of depression and anxiety (Coyne et al., 2020; Yen et al., 2018). As depression and anxiety symptoms have increased substantially in recent years as a response to the COVID-19 pandemic (Barendse et al., 2023), there is an increasing need to offer clinicians a self-assessment screening tool to assist in identifying concerning symptoms of GD with individuals seeking health care for depression and anxiety.
The WHO included GD in the ICD-11. The criteria include an inability to control gaming, prioritizing gaming over daily life activities, and continued gaming despite adverse consequences for a period of a minimum of 12 months (WHO, 2018). The DSM-5-TR includes these criteria, but offers a more robust list of symptoms. As listed in the DSM-5-TR, to meet criteria for IGD, a clinician must determine that five or more of the following symptoms are causing clinically significant impairment for the individual, and this behavior is to have occurred over a minimum of a 12-month period: (a) preoccupation with gaming, (b) withdrawal symptoms when gaming is revoked or unavailable, (c) tolerance, which is the need to spend more time gaming to satisfy the urge, (d) inability to reduce playing and unsuccessful attempts to quit gaming, (e) giving up other activities and loss of interest in previously enjoyed activities due to gaming, (f) continuing to game despite problems, (g) deceiving family members or others about the amount of time spent on gaming, (h) the use of gaming to relieve unwanted moods, such as guilt or hopelessness, and (i) risk, having jeopardized or lost a job or relationship due to gaming. While the criteria set forth by the WHO include four dimensions of criteria which are: (a) persistent, patterned, or recurrent gaming behaviors (online or offline) leading to significant impaired control over gaming behavior; (b) continually increasing priority in life being given to gaming over other areas of psychosocial functioning, daily activities, or interests/hobbies; (c) escalation of gaming behavior despite the awareness of adverse psychosocial consequences related to their increase in gaming behavior; and (d) gaming behavioral patterns result in sufficient clinically significant impairment in a person’s psychosocial functioning, which includes impacts to one or more of the following: the personal, familial, social, education, or occupational parts in their lives. Similar to the DSM-5 criteria, the WHO posits that the pattern of the gaming behavior may be episodic or recurrent, and the features and symptoms are normally evident over a period of at least 12 months in order for a diagnosis to be assigned unless the symptom severity is high and the diagnostic requirements may be met sooner.
Many measures and assessment tools have been developed to assess IGD and related concepts using criteria from the DSM-5-TR. A systematic review of 32 measures from 320 international empirical studies utilizing a variety of samples (e.g., community, gamer-specific, and general clinical populations) examined how each measure for assessment utilizing the DSM-5-TR and ICD-11 criteria for gaming disorder (King et al., 2020). The review reported how each of the 32 measure items aligned with the DSM-5-TR criteria, the ICD-11 criteria, and the shared criteria of a behavioral pattern of sufficient severity resulting in clinically significant impairment in psychosocial functioning (King et al., 2020), and results indicated that no single assessment was superior to another. The study indicated the Internet Gaming Disorder Scale–Short Form (IGDS9-SF) and Ten-Item Internet Gaming Disorder Test (IGDT-10) provided total coverage of the DSM-5-TR and ICD-11 criteria (King et al., 2020; Király et al., 2017; Pontes et al., 2017). Both the IGDT-10 and IGDSF-9 utilize a Likert-type scale to gather responses from participants based on a statement question. Each question is written using wording that encompasses a different dimension of the current nine criteria in the DSM-5-TR (e.g., preoccupation, loss of other interests, deception, escapism; King et al., 2020). Although Likert-type scales offer substantial range for clinical comprehension on severity, duration, and termination of participant responses, there is a potential limitation. Clinicians should consider an individual’s ability, as it relates to a number of factors (i.e., age, cognition, comorbid disorders), for being able to respond to concrete statements using a Likert-type scale as research suggests those with limited vocabulary or cognitive ability may lack understanding of Likert-type responses (Mellor & Moore, 2014). Furthermore, it is important to consider that terminology used with a Likert-type scoring system may not be universally compatible for patients or nonclinical staff to complete self-assessment screenings (Chen et al., 2020; King et al., 2020; Pontes et al., 2020). Thus, we examined the potential for a dichotomous self-screening that can be utilized as a precursor to a more comprehensive clinical assessment at a myriad of facilities that provide a range of services to individuals of all cognitive abilities.
Patients in clinical treatment settings often present with a myriad of symptomologies, which align with diagnostic criteria for various mental health diagnosis. A number of research studies among a variety of samples consistently report associations between GD and other mental health symptoms and disorders include depression, anxiety, obsessive–compulsive tendencies, autism spectrum disorder (ASD), and attention-deficit/hyperactivity disorder (ADHD; Andreassen et al., 2016; Bonnaire & Baptista, 2019; Colder Carras et al., 2020; Fazeli et al., 2020; Torres-Rodríguez et al., 2018). Furthermore, a large-scale general population study revealed significant relationships between ADHD, obsessive-compulsive disorder, anxiety, depression, and GD stating that for effective clinical treatment, these comorbidities need to be considered (Andreassen et al., 2016). Recognized risk factors for GD include anxiety, depression, ADHD, and exhibiting deteriorating psychiatric symptoms, which may also impact the severity of GD (Nakayama et al., 2017). Yet, many of the studies reporting associated comorbidities and risk factors stem from nonclinical samples (e.g., Andreassen et al., 2016; Bonnaire & Baptista, 2019; Feng et al., 2017). As GD is a recognized mental health diagnostic classification set forth by the WHO and APA (as noted in the ICD-11 and DSM-5-TR, respectively), the clinical importance of GD and associated comorbid issues has increased substantially yet there continues to be a lack of studies utilizing a clinical sample. Considering the aforementioned, clinicians treating patients with comorbid disorders in a treatment center for technology-based behavioral addictions utilized grounding theory and the IGD criteria in the DSM-5 to develop the Brief Internet Gaming Screen–8 (BIGS-8). Thus, this study aims to examine the clinical utility of the BIGS-8 screening tool as a self-administered tool to be utilized as a prescreening to a more comprehensive clinical assessment with individuals experiencing comorbid symptoms or disorders commonly associated with GD.
Data from the present study were derived from clinical records data in an electronic medical record (EMR) system. Clinicians consisting of licensed psychologists, social workers, counselors, and marriage and family therapists gathered and entered the data into the EMR within each patient record. Data points were collected during the initial admission of the clinical sample into a residential health care facility located in the Northwestern region of the United States from the Year 2018 to 2022. The facility specializes in treating gaming disorder and other technology-based behavioral addictions. Four independent researchers adhered to the U.S. privacy acts for protected patient health information and the federal Health Insurance Portability and Accountability Act (HIPAA) for completing the process of gaining access to the EMR. The independent researchers inspected the 128 patient records for data points and individually transferred the variables into a password protected data sheet in an encrypted database and data points were cross-checked among the researchers for quality control.
Participants (N = 128) were between the ages of 13 and 35, with one third (32%) ranging between ages 18 and 22 (Mage = 22.47, SDage = 4.29). The primary behavior associated with clinically significant impairment in their psychosocial functioning varied, with the majority (88%) self-declaring video gaming, followed by social media and internet usage (e.g., social media, shopping, or information seeking; 8.5%) and a small amount (3.5%) for internet gambling or pornography. The majority of the sample (94.5%) identified as male and as single and never married (93.8%). The sample included a large proportion identifying as White (88.3%), followed by Asian (8.6%), with one individual identifying as Black and three identifying as “other.” The inclusion criteria for the study were being admitted to the treatment facility for a minimum of 2 weeks, and purposive sampling technique was utilized to approach patients with GD and technology-based behaviors. The length of stay in treatment varied, with more than half (58.4%) remaining in treatment for a maximum of 130 days, approximately 4.5 months. Treatment phases were divided into three categories, which included (a) residential inpatient, (b) intensive outpatient, and (c) outpatient treatment. Three fourths of the sample (76%) received residential inpatient treatment (32.2% received intensive outpatient and 13.2% received outpatient treatment). Patients presented with comorbidities (as noted in Table 1) often presented with two or more diagnoses of ADHD (15.7%), anxiety (14.1%), conduct disorder/impulse control disorder (92%), ASD (10.1%), and posttraumatic stress disorder (PTSD; 3.1%). Data collection comprised four independent researchers adhering to HIPAA federal mandates to collect and develop a database from clinician input gathering in an EMR. Due to privacy laws regarding personal health records information in the United States, the data set is not publicly available.
Co-Occurring Diagnosis With Problematic Gaming
Items for the BIGS-8 were adopted from prior research and aligned with DSM-5-TR criteria on gaming disorder. The BIGS was developed by clinicians using the DSM-5-TR proposed criteria for IGD. Items were cross-referenced with the treatment facility’s program evaluation data and compared with gambling addiction screenings. Discussion among clinicians was held utilizing grounding theory as the overarching framework for the social process of exploring behavioral patterns with patients who have comorbidities with GD. Common themes identified from the discussions were systematically examined within the context of the DSM-5 criteria, which led to the development of the BIGS screening tool. Dale–Chall Readability Formula (Crossley et al., 2022) was utilized to examine recommended age for the self-assessment screening tool. The Dale–Chall Readability adjusted score is 9.6, which indicates a grade level of 13, or freshman year of college in the United States (i.e., Age 18), is recommended for comprehension (Crossley et al., 2022). Within the sample, patients who were under the age of 18 were administered the BIGS-8 by a trained staff. Participants over the age of 18 completed the BIGS-8 autonomously using article and pencil. They were instructed to choose a single response to each item regarding their video gaming behavior, answering within the context of the past 12 months. The BIGS-8 items examine the individual’s behavior of gaming over the past 12 months pertaining to increasing engagement with gaming, decreasing engagement in non-game-related activities, engaging in gaming despite psychosocial concerns that may have been exacerbated by gaming behaviors, and seeking gaming as a method to alter a mood or prevent an unwanted emotion (e.g., “Do you find yourself participating in gaming activities to feel better, e.g., reduce anxiety, loneliness, sadness, guilt, worry?”; see Appendix, for full tool). The item responses are dichotomous, with yes = 1 and no = 0.
The subscales depression and anxiety of the Depression Anxiety Stress Scale–21 (DASS-21; Lovibond & Lovibond, 1995) assessed symptoms of depression and anxiety with, respectively, seven items per subscale (e.g., Depression subscale: “I felt that life was meaningless”; Anxiety subscale: “I felt scared without any good reason”). Items are rated on a 4-point Likert-type scale (0 = did not apply to me at all, 3 = applies to me very much or most of the time). Higher sum scores indicate higher symptoms of depression and anxiety. The internal consistency of both scales was Cronbach’s α = .901 for depression symptoms and Cronbach’s α = .748 for anxiety symptoms.
Preliminary data analyses to test for assumptions of normality and bivariate outliers and descriptive statistics were conducted using the Statistical Package for the Social Sciences (SPSS Version 26; IBM Corp, 2019) and the statistical software Analysis of Moment Structures (AMOS Version 27; Arbuckle, 2014). The scale properties of the BIGS-8 were assessed by the calculation of internal consistency (Cronbach’s α), mean interitem correlation (rmi), item-total scale correlation (rit), and percent who endorsed each item (pm). Notably, in dichotomous items, pm corresponds to the item mean score. Next, the convergent validity of the BIGS-8 was investigated by examining the relationship between depression and anxiety symptoms as measured with the subscales of the DASS-21 assessed by the calculation of zero-order bivariate correlation analyses. Available research advised not to calculate an exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA) with the same sample (Field, 2013; Schmitt, 2011). Thus, to investigate the factor structure of the BIGS-8, the overall sample was randomly divided into two subsamples (n = 64). While an EFA using principal component analysis (rotation method: varimax) on the eight BIGS-8 items was calculated with one of the subsamples, a CFA was calculated with the other subsample. The CFA tested the one-factor structure of the BIGS-8 revealed by the EFA. To comprehensively examine our results of the chi-square test (χ2), we took into consideration the fit indices such as comparative fit index (CFI), standardized root-mean-square error of approximation (RMSEA), and standardized root-mean-square residual (SRMR) to assess the goodness of the model (Schermelleh-Engel et al., 2003).
Bartlett’s test of sphericity for the overall measure indicated homogeneity of variances. As shown in Table 1, the majority of patients were admitted with a diagnosis of depression (21.1%), ADHD (15.7%), anxiety (14.1%), conduct disorder/impulse control disorder (92%), ASD (10.1%), and PTSD (3.1%). Missing data for admitted diagnosis were excluded.
Table 2 shows the statistics and scale properties of the eight BIGS-8 items. The internal consistency of the BIGS-8 was good (α = .804). The mean total score for all participants was five (SD = 2.5, indicating the majority meet the proposed DSM-5-TR cutoff score criteria of five criteria). The respective exclusion of single items did not provide significant improvement in the internal consistency. The reliability ranged between α = .764 (exclusion Item 5) and α = .800 (exclusion Item 2; see Table 2). The mean interitem correlation was rmi = .338 (range: .200–.559). The item-total scale correlations were acceptable ranging between rit = .389 (Item 2) and rit = .631 (Item 7). As presented in Table 2, the percent that endorsed each item was also acceptable ranging between pm = 47% (Item 4) and pm = 77% (Item 7; Field, 2013).
Statistics and Properties of the BIGS-8 Items
M (SD)/p m
α without item
BIGS-8: Item 1
BIGS-8: Item 2
BIGS-8: Item 3
BIGS-8: Item 4
BIGS-8: Item 5
BIGS-8: Item 6
BIGS-8: Item 7
BIGS-8: Item 8
The sum score of the BIGS-8 (M = 4.44, SD = 2.54; range: 0–8) was significantly positively correlated with the sum score of the DASS-21 depression subscale (M = 7.11, SD = 5.89; range: 0–21, r = .278, p = .018). Moreover, it was significantly positively correlated with the sum score of the DASS-21 anxiety subscale (M = 2.92, SD = 3.30; range: 0–15, r = .234, p = .044), but not significantly positively correlated with sum score of the DASS-21 stress subscale. These results reveal an acceptable convergent validity of the BIGS-8 (Field, 2013).
A parallel analysis scree plot was conducted to compare 1,000 observed eigenvalues to the 95th percentile of permuted eigenvalues for determining factor retention (see Figure 1; Costello & Osborne, 2005; Velicer & Jackson, 1990). As the first raw data eigenvalue is higher than the permuted eigenvalue, and the second eigenvalue is lower than the permuted eigenvalue, the parallel analysis supports a one-factor solution (Figure 1). In addition, the Kaiser’s eigenvalue greater than one criterion supported a one-factor solution. The EFA (Kaiser–Meyer–Olkin: KMO = .851; Barlett’s test of sphericity: χ2 = 136.373, df = 28, p < .001) revealed a one-factor structure of the BIGS-8. The factor had an eigenvalue of 3.620 and explained 45.26% of the variance. As shown in Table 2, the loadings of the items ranged between .616 (Item 2) and .747 (Item 3). The scree plot presented in Figure 1 visualizes the one-factor structure of the BIGS-8.
The CFA confirmed the one-factor structure of the BIGS-8. It resulted in a not significant chi-square value (χ2 = 24.353, df = 20, p = .227). The three included fit indices revealed an acceptable to good model fit: CFI = .960, RMSEA = .059 (90% CI [.000, 129]), and SRMR = .067 (Bentler, 1990; Hooper et al., 2008; Hu & Bentler, 1998; Shi et al., 2019).
Uniformity within a diagnostic system is one way in which assessment and screening tools ensure consistency across professional usages. The BIGS-8 is a screening tool designed with this conceptualization in mind by considering the association of comorbidities among those with GD. In keeping with a components-based addiction framework, the BIGS-8 was able to yield an acceptable model fit while considering the presence of eight common addiction elements explored by clinicians in residential treatment modalities across the world and present in those with comorbidities. Utilizing a self-screening tool with accessible language allows for enhanced patient self-awareness, which is paramount in the treatment of problematic behaviors (Maracic & Moeller, 2021). Furthermore, the BIGS-8 is significantly correlated with signs and symptoms of depression and anxiety, as evidenced by the DASS-21 subscale scores, which often co-occur with GD (Peter et al., 2020).
Within this study, the population being served is critical for understanding the extent of appropriate application for a tool such as the BIGS-8. The sum score of the BIGS-8 was significantly positively correlated with the sum score of the DASS-21 Depression subscale and the DASS-21 Anxiety subscale. This indicates that there is alignment with the BIGS-8 and comorbidities of depression and anxiety, which are commonly associated with GD among nonclinical samples (Andreassen et al., 2016; Bonnaire & Baptista, 2019; Colder Carras et al., 2020; Fazeli et al., 2020). Thus, this further provides evidence of the convergent validity for the BIGS-8 as depression and anxiety are often highly correlated among varying international samples of those with GD, as well as having been identified as risk factors for GD (Gao et al., 2022).
The identification of presenting comorbid primary and secondary diagnoses (i.e., ADHD, conduct disorder, ASD, etc.) within the present study highlights the comorbid issues for which treatment is often sought, further indicating the importance of clinicians incorporating screening for GD and video gaming-related issues, as the severity of impairment shaped by problematic behaviors in biopsychosocial functioning may not be initially identified by the client and/or their family system (Maracic & Moeller, 2021). It is notable that intentionally developed self-assessment screening tools can serve as a precursor to a more comprehensive clinician-led standardized measure for assessment. When conducting more thorough assessments, clinicians need to be proficient at considering comorbid disorders among those with symptoms of GD. Thus, it is essential to consider possible associations and correlations between signs and symptoms across two or more mental health disorders. Yet, this can be challenging as there are a vast array of comorbidities and due to the varying perspectives, models, and approaches utilized by clinical professions (e.g., psychiatry, social work, counseling, nursing) who are able to diagnose and/or screen for GD, having a screening tool that aligns with frequent comorbidities can aid in their diagnostic processes.
The majority of this sample was within the age range of 18–22, which aligns with the age group most often found to have higher prevalence rates of GD (Stevens et al., 2021). In addition, this age group is predominantly among those who frequently engage in video game playing (ESA, 2021). Overall, results provide support for the BIGS-8 as a self-assessment screening tool for assessing GD. More specifically, the results from the parallel analysis scree plot supported a one-factor solution. All factor loadings were significant above 0.60, and the internal consistency for the overall measure was good (α = .802; Tavakol & Wetzel, 2020). The results from the EFA and CFA also supported a one-factor solution. Therefore, the BIGS-8 has potential for providing insight into the severity of functional impairment for those experiencing GD and comorbidities. Reponses from the BIGS-8 screening tool can be utilized to inform individuals and health care staff of the potential need for a more comprehensive clinical assessment utilizing a robust valid and reliable measure to provide a diagnosis of GD, as well as for identifying presenting life limitations and aiding in the process of optimizing treatment at the psychological and various holistic levels.
This study contributes to the GD literature by utilizing a clinical sample with comorbidities for exploring the potential of a self-assessment screening tool for adults; however, important limitations should be noted. First, the data are limited as it was derived from current existing information within an EMR utilized in a clinical setting comprised of individuals mandated to treatment and voluntarily to treatment. Second, the EMR data lacked a valid and reliable measure for conduct disorder and, as much of sample where diagnosed with conduct disorder, future research is needed to examine convergent validity between the BIGS-8 and a validated scale for conduct disorder. Additionally, this study serves as preliminary research to examine a self-assessment screening tool for GD, and further research is needed that provides more rigorous approaches to validate the screening tool, as well as to provide clinical parameters for identification of the behavior (i.e., nondisordered/leisure gaming, risky/problematic gaming, and gaming disorder). Although the EFA and CFA were conducted across two randomized participant groups, all were recruited from one residential treatment facility. Considering the small size of the subsamples, the results of the EFA and the CFA should be interpreted with caution (Schermelleh-Engel et al., 2003). Furthermore, the sample is not representative of all clinical treatment facilities in the United States or homogeneous with the demographics found within the geographical location of the facility as the majority of the sample identified as White (88.3%) followed by the broad categorization of Asian (8.6%). It is important for future studies to intentionally utilize sampling methodologies to gather data with more diverse representative demographics. Also, future studies are needed to replicate the one-factor solution using additional independent samples with comparison groups of clinical and nonclinical samples. Given the data points being restricted to availability within the EMR and the cross-sectional nature of the study, certain aspects of reliability (e.g., test–retest) and validity (e.g., predictive validity) were not tested. Despite these limitations, the present study is one of few efforts to examine the use of a self-assessment screening measure for gaming disorder to be utilized with those who have comorbidities in a health care setting.
Instructions: The following questions ask about your video gaming behavior, both online and offline, over the past 12 months. Choose the answer that you feel best describes your experience.
2. Has your engagement with gaming activities increased in the past year?
3. Have you tried to reduce participation in game activities but found it too difficult, so you’ve continued engaging in gaming activities?
4. Have you lost interest in non-game-related activities (e.g., sports, hobbies, family, activities)?
5. Have you continued to engage in game activities despite knowing the problems you experience as a result of your use?
6. Have you deceived a family member, significant other, employer, or therapist regarding the amount of time spent engaging in gaming activities?
7. Do you find yourself participating in gaming activities to feel better (e.g., reduce anxiety, loneliness, sadness, guilt, worry)?
8. Have you jeopardized or lost a significant relationship, academic or employment opportunity because of your engagement with gaming activities?