ABSTRACT
BACKGROUND: In recent years, there have been frequent reports of gaming disorder in China, with more focus on young people. We developed and psychometrically tested a Gaming Disorder screening scale (i.e., Gaming Disorder Screening Scale - GDSS) for Chinese adolescents and young adults, based on the existing scales and diagnostic criteria, but also considering the development status of China. METHODS: For testing content and criterion validity, 1747 participants competed the GDSS and the Internet Addiction Test (IAT). After 15 days, 400 participants were retested with the scales for to assess test-retest reliability. Besides, 200 game players were interviewed for a diagnosis of gaming disorder. RESULTS: The Cronbach's alpha coefficient on the GDSS was 0.93. The test-retest coefficient of 0.79. Principal components analysis identified three factors accounting for 62.4% of the variance; behavior, functioning, cognition and emotion. Confirmatory factor analysis showed a good model fit to the data (χ2 /df = 5.581; RMSEA =0.074; TLI = 0.916, CFI = 0.928). The overall model fit was significantly good in the measurement invariance tested across genders and different age groups. Based on the clinical interview, the screening cut-off point was determined to be ≥47 (sensitivity 41.4%, specificity 82.3%). CONCLUSIONS: The GDSS demonstrated good reliability and validity aspects for screening online gaming disorder among Chinese adolescents and young adults.
Subject(s)
Behavior, Addictive , Video Games , Adolescent , Behavior, Addictive/diagnosis , Behavior, Addictive/epidemiology , Behavior, Addictive/psychology , China , Factor Analysis, Statistical , Female , Humans , Male , Psychometrics , Reproducibility of Results , Surveys and Questionnaires , Video Games/psychology , Young AdultABSTRACT
Purpose: As a new category proposed in the International Classification of Diseases (11th Revision) (ICD-11), the reliability and clinical utility of ICD diagnostic guidelines for gaming disorder (GD) in the Chinese population have not been studied. The purpose of this field study is to clarify the reliability, clinical utility, and cultural applicability of ICD diagnostic guidelines for GD in China and its comparability with Internet GD (IGD) in the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5). Methods: Participants included 21 paired clinical raters consisting of seven psychiatrists and 200 gaming players aged from 15 to 18 years with different risk levels of Internet addiction based on the scores of Young's Internet Addiction Test. Each participant received a semi-structured face-to-face interview by paired clinical raters at the same time. Then clinical raters made the diagnosis and filled the clinical utility questionnaire independently according to the diagnostic guidelines for GD in both ICD-11 and DSM-5. Results: The diagnostic consistency coefficient (kappa value) between the paired clinical raters was 0.545 (0.490-0.600, p < 0.001) and 0.622 (0.553-0.691, p < 0.001) for ICD-11 and DSM-5 diagnostic guidelines, respectively, for GD. The diagnostic consistency was 0.847 (0.814-0.880, p < 0.001) between GD in ICD-11 and IGD in DSM-5. Meanwhile, 86.7% of responses that agreed with the ICD-11 diagnostic guidelines for GD provided enough detailed implementation characteristics and showed good overall clinical applicability (86.0%), specificity (94.4%), usefulness (84.1%), and acceptable cultural adaptation (74.8%). GD in ICD-11 was slightly more accepted than IGD in DSM-5 (p < 0.001), while the clinical efficiency of ICD-11 was inferior to that of DSM-5 (p < 0.001). Conclusion: This study indicates that the ICD-11 diagnostic guidelines for GD have acceptable clinical reliability and high consistency with IGD in DSM-5. Their clinical applicability and cultural adaption are comparable with those of DSM-5. Although the guidelines still need to be adjusted for better implementation in China, this is already a great step committed to reducing the serious consequences caused by excessive gaming behaviors through effective identification and normative diagnosis, especially for adolescents.
ABSTRACT
BACKGROUND: Accumulated studies in substance use disorders indicated that the mesocorticolimbic reward circuit and executive control circuit played interactive roles in the cue-reactivity and gamma oscillatory in the cortex, as well as autonomic nervous activity was associated with it. AIM OF THE STUDY: This study aimed to explore the mechanism of processing drug-related cues. METHODS: Sixty methamphetamine users were recruited and exposed to the drug-related virtual reality (VR) environments. Self-reported craving was assessed and skin conductance level (SCL) and heart rate variability were used to measure the autonomic nervous reactivity. With source estimation, gamma current density was used to measure the activity of medial prefrontal cortex/orbitofrontal cortex (MPFC/OFC) and dorsolateral prefrontal cortex (DLPFC) which are responsible for the mesocorticolimbic reward circuit and the executive control circuit. RESULTS: The VR drug-related cue induced an increased craving and SCL compared with the neutral condition. Gamma activity in MPFC/OFC and right DLPFC were decreased after cue exposure and predicted the SCL changes. However, no association was found between self-reported craving and electrophysiological response. CONCLUSIONS: Cue-reactivity in cortex and ANS, as well as their association, were observed in this study. Cue-induced electrophysiological responses were related to gamma activity in the mesocorticolimbic reward circuit and the executive control circuit.
Subject(s)
Central Nervous System Stimulants/administration & dosage , Craving/physiology , Executive Function/drug effects , Methamphetamine/administration & dosage , Substance-Related Disorders/physiopathology , Virtual Reality , Adolescent , Adult , Brain Mapping/methods , Cues , Electrophysiology , Executive Function/physiology , Female , Heart Rate/physiology , Humans , Male , Middle Aged , Prefrontal Cortex/physiology , Reward , Substance-Related Disorders/psychology , Young AdultABSTRACT
BACKGROUND: Cognitive rehabilitation therapy has been found to improve cognitive deficits and impulse control problems in methamphetamine use disorder (MUD). However, there is limited research regarding this therapy's feasibility when using mobile-based health technologies in supporting recovery from MUD in China. OBJECTIVE: The main aim of this study was to test whether 4 weeks of a newly designed computerized cognitive addiction therapy (CCAT) app can improve cognitive impairments, eliminate drug-related attention bias, and attenuate risk decision-making behaviors in participants with MUD. METHODS: Forty MUD participants were assigned randomly to either the CCAT group (n=20), who received 4 weeks of CCAT plus regular detoxification treatment as usual, or the control group (n=20), who only received the regular detoxification treatment as usual, in drug rehabilitation centers in Shanghai. The CCAT was designed by combine methamphetamine use-related picture stimuli with cognitive training with the aim of improving cognitive function and eliminating drug-related attention bias. The CogState Battery, Delay Discounting Task (DDT), Iowa Gambling Task (IGT), and Balloon Analog Risk Task (BART) were administered face-to-face to all participants before and after CCAT interventions. RESULTS: Forty male patients were recruited. The mean age was 32.70 (SD 5.27) years in the CCAT group and mean 35.05 (SD 8.02) years in the control group. Compared to the control group, CCAT improved working memory in the CCAT group (P=.01). Group×time interactions were observed among DDT, IGT, and BART tasks, with rates of discounting delayed rewards, IGT, and BART scores (P<.001) being reduced among those who received CCAT, whereas no changes were found in the control group. CONCLUSIONS: The newly designed CCAT can help to improve cognitive impairment and impulsive control in MUD. Further study is needed to understand the underlying brain mechanisms of the cognitive therapy. TRIAL REGISTRATION: ClinicalTrials.gov NCT03318081; https://clinicaltrials.gov/ct2/show/NCT03318081 (Archived by WebCite at https://clinicaltrials.gov/ct2/show/NCT03318081).