The reliability and validity of a screening scale for online gaming disorder among Chinese adolescents and young adults

Network analysis of additional clinical features of (Internet) gaming disorder

OBJECTIVES

There are dozens of screening instruments purporting to measure the (Internet) gaming disorder (IGD/GD). The two prominent diagnostic manuals, DSM-5 and ICD-11, list several additional diagnostic or clinical features and problems (e.g., neglect of sleep, neglect of daily duties, health deterioration) that should co-occur or be caused by the IGD/GD. It remains unclear how specific IGD/GD operationalizations (different screening scales) are related to these functional impairments.

METHODS

To explore this, data on six measures of IGD/GD (IGDS9-SF, GDSS, GDT, GAMES test, two self-assessments) and 18 additional diagnostic features were collected from a sample of 1009 players who play digital games at least 13 h per week. A network approach was utilized to determine which operationalization is most strongly associated with functional impairment.

RESULTS

In most of the networks, IGD/GD consistently emerged as the most central node.

CONCLUSION

The similar centrality of IGD/GD, irrespective of its definition (DSM-5 or ICD-11) or operationalization, provides support for the valid comparison or synthesis of results from studies that used instruments coming from both DSM-5 and ICD-11 ontologies, but only if the goal is to evaluate IGD/GD relationships to other phenomena, not the relationships between the symptoms themselves.

Disrupted inter-brain synchronization in the prefrontal cortex between adolescents and young adults with gaming disorders during the real-world cooperating video games

BACKGROUND

Gaming disorder (GD) and hazardous gaming (HG) have a high incidence among adolescents and young adults and have caused various negative consequences. Interpersonal interaction deficits are closely related to GD and HG, however, the underlying brain mechanisms are still unclear.

METHODS

The current study recruited 46 healthy subjects and 32 subjects with GD/HG. Gaming time and frequency, gaming disorder risks, life events, strengths, and difficulties were measured with scales. Subjects were randomly paired into 12 HC-HC dyads, 15 GD/HG-HC dyads, and 7 GD/HG-GD/HG dyads and in pairs completed a real-world cooperating video game - "Tied Together" with functional near-infrared spectroscopy hyperscanning recording in the prefrontal cortex. The inter-brain synchronization in each region of the PFC between dyads was calculated by wavelet to transform coherence to measure brain-to-brain synchronization.

RESULTS

We found subjects with GD/HG reported higher risks of gaming. The highest IBS in the left dorsolateral prefrontal cortex significantly decreased in the GD/HG-HC and GD/HG-GD/HG dyads compared with healthy controls. A decreasing highest IBS of the left dlPFC was related to a decreasing level of peer problems.

LIMITATIONS

We declare limitations of age gaps of samples, undistinguishing GD from HG, use of sub-samples, and the broad concept of interpersonal interaction.

CONCLUSIONS

The current study found a decreased highest IBS in the left dlPFC among adolescents and young adults with gaming diseases. It may provide new prevention and treatment insights into gaming disorders targeting disrupted interpersonal interaction.

Deep neural network to differentiate internet gaming disorder from healthy controls during stop-signal task: a multichannel near-infrared spectroscopy study

Internet Gaming Disorder (IGD), as one of worldwide mental health issues, leads to negative effects on physical and mental health and has attracted public attention. Most studies on IGD are based on screening scales and subjective judgments of doctors, without objective quantitative assessment. However, public understanding of internet gaming disorder lacks objectivity. Therefore, the researches on internet gaming disorder still have many limitations. In this paper, a stop-signal task (SST) was designed to assess inhibitory control in patients with IGD based on prefrontal functional near-infrared spectroscopy (fNIRS). According to the scale, the subjects were divided into health and gaming disorder. A total of 40 subjects (24 internet gaming disorders; 16 healthy controls) signals were used for deep learning-based classification. The seven algorithms used for classification and comparison were deep learning algorithms (DL) and machine learning algorithms (ML), with four and three algorithms in each category, respectively. After applying hold-out method, the performance of the model was verified by accuracy. DL models outperformed traditional ML algorithms. Furthermore, the classification accuracy of the two-dimensional convolution neural network (2D-CNN) was 87.5% among all models. This was the highest accuracy out of all models that were tested. The 2D-CNN was able to outperform the other models due to its ability to learn complex patterns in data. This makes it well-suited for image classification tasks. The findings suggested that a 2D-CNN model is an effective approach for predicting internet gaming disorder. The results show that this is a reliable method with high accuracy to identify patients with IGD and demonstrate that the use of fNIRS to facilitate the development of IGD diagnosis has great potential.