Detection of Craving for Gaming in Adolescents with Internet Gaming Disorder Using Multimodal Biosignals

Physiological Regularity and Synchrony in Individuals with Gaming Disorder

Individuals with gaming disorder (GD) show emotional dysregulation and autonomic dysfunction in daily life. Although studies have shown that the relaxation method of breathing exercise (BE) improves cardiopulmonary synchrony, the physiological regularity and synchrony of GD remain unclear. In this study, we investigated the regularities of pulse wave (PW), thoracic wall movement (TWM), and abdominal wall movement (AWM) using sample entropy (SE) and assessed the vascular-respiratory and TWM-AWM synchrony using cross-sample entropy (CSE). Twenty individuals with GD and 26 healthy control (HC) individuals participated in baseline, gaming, and recovery stages, both before and after BEs. The results showed that both groups had significantly higher SETWM, SEAWM, and CSETWM-AWM during gaming than baseline. Before BE, CSEPW-TWM and CSEPW-AWM during gaming were considerably higher in the GD group than in the HC group. Compared to before BE, both groups had decreased SETWM and CSETWM-AWM during gaming, particularly in the HC group. Online gaming may induce pulse wave and respiratory irregularities, as well as thoracic-abdominal wall movement asynchrony. Individuals with GD who engage in prolonged gaming periods may exhibit lower vascular-respiratory synchrony compared to the HC group. SETWM, SEAWM, CSETWM-AWM, CSEPW-TWM, and CSEPW-AWM may serve as biomarkers for assessing the risk of GD. BE may improve TWM regularity and vascular-respiratory synchrony during gaming, potentially alleviating addictive behavior.

Biomarkers of Internet Gaming Disorder-A Narrative Review

Since game mechanics and their visual aspects have become more and more addictive, there is concern about the growing prevalence of Internet gaming disorder (IGD). In the current narrative review, we searched PubMed and Google Scholar databases for the keywords "igd biomarker gaming" and terms related to biomarker modalities. The biomarkers we found are grouped into several categories based on a measurement method and are discussed in the light of theoretical addiction models (tripartite neurocognitive model, I-PACE). Both theories point to gaming-related problems with salience and inhibition. The first dysfunction makes an individual more susceptible to game stimuli (raised reward seeking), and the second negatively impacts resistance to these stimuli (decreased cognitive control). The IGD patients' hypersensitivity to reward manifests mostly in ventral striatum (VS) measurements. However, there is also empirical support for a ventral-to-dorsal striatal shift and transition from goal-directed to habitual behaviors. The deficits in executive control are demonstrated in parameters related to the prefrontal cortex (PFC), especially the dorsolateral prefrontal cortex (DLPFC). In general, the connection of PFC with reward under cortex nuclei seems to be dysregulated. Other biomarkers include reduced P3 amplitudes, high-frequency heart rate variability (HRV), and the number of eye blinks and saccadic eye movements during the non-resting state. A few studies propose a diagnostic (multimodal) model of IGD. The current review also comments on inconsistencies in findings in the nucleus accumbens (NAcc), anterior cingulate cortex (ACC), and precuneus and makes suggestions for future IGD studies.

Altered Heart Rate Variability During Mobile Game Playing and Watching Self-Mobile Gaming in Individuals with Problematic Mobile Game Use: Implications for Cardiac Health

Introduction

The surge in mobile gaming, fueled by smartphone and internet accessibility, lacks a comprehensive understanding of physiological changes during gameplay.

Methods

This study, involving 93 participants (average age 21.75 years), categorized them into Problematic Mobile Gaming (PMG) and non-problematic Mobile Gaming (nPMG) groups based on Problematic Mobile Gaming Questionnaire (PMGQ) scores. The PMGQ is a 12-item scale developed in Taiwan to assess symptoms of problematic mobile gaming. The research delved into heart rate variability (HRV) alterations during real-time mobile gaming and self-gaming video viewing.

Results

Results showed that the PMG group significantly presents a lower root mean square of successive differences (RMSSD), and High Frequency (lnHF) than does the nPMG group (F=4.73, p=0.03; F=10.65, p=0.002, respectively) at the baseline. In addition, the PMG group significantly displayed elevated HF and low-frequency to high-frequency (LF/HF) in the mobile-gaming (F=7.59, p=0.007; F=9.31, p=0.003) condition as well as in the watching self-gaming videos (F=9.75, p=0.002; F=9.02, p=0.003) than did the nPMG.

Conclusion

The study suggests targeted interventions to mitigate autonomic arousal, offering a potential avenue to address adverse effects associated with problematic mobile gaming behavior. The PMG group displayed increased craving scores after real-time mobile gaming and watching self-gaming video excerpts, unlike the nPMG group. Elevated LF/HF ratios in frequent gaming cases heightened autonomic arousal, presenting challenges in relaxation after mobile gaming. These findings contribute to a nuanced understanding of the complex interplay between mobile gaming activities, physiological responses, and potential intervention strategies.

Neural responses to gaming content on social media in young adults

Excessive gaming can impair both mental and physical health, drawing widespread public and clinical attention, especially among young generations. People are now more exposed to gaming-related content on social media than before, and this exposure may have a significant impact on their behavior. However, the neural mechanisms underlying this effect remain unexplored. Using functional magnetic resonance imaging (fMRI), this study aimed to investigate the neural activity induced by gaming-related content on social media among young adults casually playing online games. While being assessed by fMRI, the participants watched gaming-related videos and neutral (nongaming) videos on social media. The gaming-related cues significantly activated several brain areas, including the medial prefrontal cortex, posterior cingulate cortex, hippocampus, thalamus, superior/middle temporal gyrus, precuneus and occipital regions, compared with the neutral cues. Additionally, the participants' gaming desire levels positively correlated with a gaming-related cue-induced activation in the left orbitofrontal cortex and the right superior temporal gyrus. These findings extend previous studies on gaming cues and provide useful information to elucidate the effects of gaming-related content on social media in young adults. Continued research using real-world gaming cues may help improve our understanding of promoting gaming habits and provide support to individuals vulnerable to gaming addiction.

AR-Enabled Persistent Human-Machine Interfaces via a Scalable Soft Electrode Array

Augmented reality (AR) is a computer graphics technique that creates a seamless interface between the real and virtual worlds. AR usage rapidly spreads across diverse areas, such as healthcare, education, and entertainment. Despite its immense potential, AR interface controls rely on an external joystick, a smartphone, or a fixed camera system susceptible to lighting. Here, an AR-integrated soft wearable electronic system that detects the gestures of a subject for more intuitive, accurate, and direct control of external systems is introduced. Specifically, a soft, all-in-one wearable device includes a scalable electrode array and integrated wireless system to measure electromyograms for real-time continuous recognition of hand gestures. An advanced machine learning algorithm embedded in the system enables the classification of ten different classes with an accuracy of 96.08%. Compared to the conventional rigid wearables, the multi-channel soft wearable system offers an enhanced signal-to-noise ratio and consistency over multiple uses due to skin conformality. The demonstration of the AR-integrated soft wearable system for drone control captures the potential of the platform technology to offer numerous human-machine interface opportunities for users to interact remotely with external hardware and software.

Explicit and implicit effects of gaming content on social media on the behavior of young adults

Excessive gameplay can have negative effects on both mental and physical health, especially among young people. Nowadays, social media platforms are bombarding users with gaming-related content daily. Understanding the effect of this content on people's behavior is essential to gain insight into problematic gaming habits. However, this issue is yet to be studied extensively. In this study, we examined how gaming-related content on social media affects young adults explicitly and implicitly. We studied 25 healthy young adults (average age 21.5 ± 2.2) who played online games casually and asked them to report their gaming desire. We also conducted an implicit association test (IAT) to measure their implicit attitudes toward gaming-related content. We also investigated the relationship between these measures and various psychological factors, such as personality traits, self-efficacy, impulsiveness, and cognitive flexibility. The results revealed that participants had a higher explicit gaming desire when exposed to gaming-related cues on social media than neutral cues. They also had a robust positive implicit attitude toward gaming-related content on social media. Explicit gaming desire was positively correlated with neuroticism levels. Furthermore, the IAT effect was negatively correlated with self-efficacy and cognitive flexibility levels. However, there were no significant correlations between explicit gaming desire/IAT effect and impulsiveness levels. These findings suggest that gaming-related content on social media can affect young adults' behavior both explicitly and implicitly, highlighting the need for further research to prevent gaming addiction in vulnerable individuals.

Heart Rate Variability during Online Video Game Playing in Habitual Gamers: Effects of Internet Addiction Scale, Ranking Score and Gaming Performance

Previous studies have demonstrated that individuals with internet gaming disorder (IGD) display abnormal autonomic activities at rest and during gameplay. Here, we examined whether and how in-game autonomic activity is modulated by human characteristics and behavioral performance of the player. We measured heart rate variability (HRV) in 42 male university student habitual gamers (HGs) when they played a round of League of Legends game online. Short-term HRV indices measured in early, middle and late phases of the game were compared between the players at high risk of developing IGD and those at low risk, as assessed by the revised Chen Internet addiction scale (CIAS-R). Multiple linear regression (MLR) was used to identify significant predictors of HRV measured over the whole gameplay period (WG), among CIAS-R, ranking score, hours of weekly playing and selected in-game performance parameters. The high-risk players showed a significantly higher low-frequency power/high-frequency power ratio (LF/HF) relative to the low-risk players, regardless of game phase. MLR analysis revealed that LF/HF measured in WG was predicted by, and only by, CIAS-R. The HRV indicators of sympathetic activity were found to be predicted only by the number of slain in WG (NSlain), and the indicators of parasympathetic activity were predicted by both CIAS-R and NSlain. Collectively, the results demonstrated that risk of developing IGD is associated with dysregulated autonomic balance during gameplay, and in-game autonomic activities are modulated by complex interactions among personal attributes and in-game behavioral performance of the player, as well as situational factors embedded in game mechanics.

The latent profile analysis of Chinese adolescents' gaming disorder: examination and validation

BACKGROUND

Gaming disorder is a new disease, which is included in the disease unit of disorder caused by addiction in the 11th revision of the International Classification of Diseases. This study examined the symptom characteristics of gaming disorders in Chinese adolescents using the latent profile analysis.

METHODS

Totally, 5988 students (including 3285 boys and 2703 girls; aged 12-18 years) from junior high schools and senior high schools were enrolled. The Gaming Disorder Symptom Questionnaire-21 (GDSQ-21) was used to screen gaming disorder. A latent profile analysis was used for classifying the subgroups based on the extent of gaming usage. The relationship between adolescent gamers and demographic variables was analyzed by logistic regression.

RESULTS

The results of latent profile analysis supported the models of four latent profiles, which were defined as healthy gamers (Profile 1, 56.83%), impaired control gamers (Profile 2, 26.09%), impaired control-game priority gamers (Profile 3, 9.72%) and gamers with disorder (Profile 4, 7.36%), respectively. Logistic regression analysis found that, compared with girls, boys were more likely to be classified into the group dominated by the impaired gamers, the impaired control-game priority gamers, and the gamers with disorder.

CONCLUSIONS

This study highlighted that the latent profile analysis identified four different groups of adolescent gamers, showing a clearer conceptualization of heterogeneous gamers. Gender and average weekly gaming time can predict the latent profile of adolescents. Our findings may facilitate the design of individualized assessment and early intervention programs for adolescent gamer users based on different gaming usage symptoms.

Severity identification for internet gaming disorder using heart rate variability reactivity for gaming cues: a deep learning approach

Background

The diminished executive control along with cue-reactivity has been suggested to play an important role in addiction. Hear rate variability (HRV), which is related to the autonomic nervous system, is a useful biomarker that can reflect cognitive-emotional responses to stimuli. In this study, Internet gaming disorder (IGD) subjects' autonomic response to gaming-related cues was evaluated by measuring HRV changes in exposure to gaming situation. We investigated whether this HRV reactivity can significantly classify the categorical classification according to the severity of IGD.

Methods

The present study included 70 subjects and classified them into 4 classes (normal, mild, moderate and severe) according to their IGD severity. We measured HRV for 5 min after the start of their preferred Internet game to reflect the autonomic response upon exposure to gaming. The neural parameters of deep learning model were trained using time-frequency parameters of HRV. Using the Class Activation Mapping (CAM) algorithm, we analyzed whether the deep learning model could predict the severity classification of IGD and which areas of the time-frequency series were mainly involved.

Results

The trained deep learning model showed an accuracy of 95.10% and F-1 scores of 0.995 (normal), 0.994 (mild), 0.995 (moderate), and 0.999 (severe) for the four classes of IGD severity classification. As a result of checking the input of the deep learning model using the CAM algorithm, the high frequency (HF)-HRV was related to the severity classification of IGD. In the case of severe IGD, low frequency (LF)-HRV as well as HF-HRV were identified as regions of interest in the deep learning model.

Conclusion

In a deep learning model using the time-frequency HRV data, a significant predictor of IGD severity classification was parasympathetic tone reactivity when exposed to gaming situations. The reactivity of the sympathetic tone for the gaming situation could predict only the severe group of IGD. This study suggests that the autonomic response to the game-related cues can reflect the addiction status to the game.

Can Heart Rate Variability be Viewed as a Biomarker of Problematic Internet Use? A Systematic Review and Meta-Analysis

Heart rate variability (HRV) has been used to explore the parasympathetic activity of individuals with problematic Internet use (PIU), but the results are controversial. We conducted a systematic review and meta-analysis of studies comparing HRV in PIU individuals and healthy participants from several databases. HRV was analyzed according to the parasympathetic activity in hierarchical order (primary analysis), and the total variability (secondary analysis). The baseline HRV and HRV reactivity were both considered. Of the 106 studies screened, 12 were included in the quantitative analysis. Significant differences were observed for baseline HRV in PIU individuals compared to the controls. Regarding HRV reactivity, PIU individuals did not have a significantly lower HRV value during pleasant or unpleasant stimuli. In summary, PIU individuals and healthy subjects had significantly different resting state parasympathetic activity. The finding of HRV reactivity in PIU individuals awaits further investigation.

Classification of Gamers Using Multiple Physiological Signals: Distinguishing Features of Internet Gaming Disorder

The proliferating and excessive use of internet games has caused various comorbid diseases, such as game addiction, which is now a major social problem. Recently, the American Psychiatry Association classified “Internet gaming disorder (IGD)” as an addiction/mental disorder. Although many studies have been conducted on the diagnosis, treatment, and prevention of IGD, screening studies for IGD are still scarce. In this study, we classified gamers using multiple physiological signals to contribute to the treatment and prevention of IGD. Participating gamers were divided into three groups based on Young’s Internet Addiction Test score and average game time as follows: Group A, those who rarely play games; Group B, those who enjoy and play games regularly; and Group C, those classified as having IGD. In our game-related cue-based experiment, we obtained self-reported craving scores and multiple physiological data such as electrooculogram (EOG), photoplethysmogram (PPG), and electroencephalogram (EEG) from the users while they watched neutral (natural scenery) or stimulating (gameplay) videos. By analysis of covariance (ANCOVA), 13 physiological features (vertical saccadic movement from EOG, standard deviation of N-N intervals, and PNN50 from PPG, and many EEG spectral power indicators) were determined to be significant to classify the three groups. The classification was performed using a 2-layers feedforward neural network. The fusion of three physiological signals showed the best result compared to other cases (combination of EOG and PPG or EEG only). The accuracy was 0.90 and F-1 scores were 0.93 (Group A), 0.89 (Group B), and 0.88 (Group C). However, the subjective self-reported scores did not show a significant difference among the three groups by ANCOVA analysis. The results indicate that the fusion of physiological signals can be an effective method to objectively classify gamers.

Extended classifier system with continuous real-coded variables for feature extraction of instantaneous pulse-rate variability and respiration of individuals with gaming disorder

Background

Individuals with gaming disorder (GD) exhibit autonomic nervous system responses that indicate dysfunctional emotion regulation. Pulse rate variability (PRV) is a valuable biomarker for investigating the autonomic function of patients with mental disorders. Because individuals with GD dynamically regulate emotions during gaming, the PRV response relating to GD is not well understood. To investigate the dynamic PRV responses of individuals with GD, this study proposed the indexes of instantaneous PRV (iPRV) and instantaneous respiratory frequency (IF_resp) of arterial blood pressure signals using empirical mode decomposition and normalized direct-quadrature algorithms. iPRV consists of low-frequency (LF), high-frequency (HF), and very high-frequency (VHF) bands. Moreover, a novel method of extended classifier system with continuous real-coded variables (XCSR) was used to detect GD and extract GD-related iPRV features using iPRV and IF_resp as input data.

Results

A total of 32 college students without depressive and anxiety symptoms or cardiovascular diseases were recruited in this study. Participants were grouped into the high-risk GD and low-risk GD using both Chen Internet Addiction Scale and Internet Gaming Disorder Questionnaire. Their arterial blood pressures signals were measured while they watched gameplay videos with negative or positive emotional stimuli. Seven participants with high-risk GD exhibited significantly increased normalized VHF (nVHF) PRV and IF_resp readings and significantly decreased normalized LF (nLF) PRV readings and LF/HF PRV ratios (from baseline) during negative or positive gameplay videos stimuli. These participants also exhibited higher nVHF PRV and lower nLF PRV readings and LF/HF PRV ratios when they experienced negative gameplay video stimuli relative to 17 participants with low-risk GD. The classification accuracy of the XCSR reached 90% for both negative and positive video stimuli, and nVHF PRV was most frequently used to detect GD risk.

Conclusions

iPRV and IF_resp can be used to detect GD and analyze the autonomic mechanism of individuals with GD.

Identification of Video Game Addiction Using Heart-Rate Variability Parameters

The purpose of this study is to determine heart rate variability (HRV) parameters that can quantitatively characterize game addiction by using electrocardiograms (ECGs). 23 subjects were classified into two groups prior to the experiment, 11 game-addicted subjects, and 12 non-addicted subjects, using questionnaires (CIUS and IAT). Various HRV parameters were tested to identify the addicted subject. The subjects played the League of Legends game for 30–40 min. The experimenter measured ECG during the game at various window sizes and specific events. Moreover, correlation and factor analyses were used to find the most effective parameters. A logistic regression equation was formed to calculate the accuracy in diagnosing addicted and non-addicted subjects. The most accurate set of parameters was found to be pNNI20, RMSSD, and LF in the 30 s after the “being killed” event. The logistic regression analysis provided an accuracy of 69.3% to 70.3%. AUC values in this study ranged from 0.654 to 0.677. This study can be noted as an exploratory step in the quantification of game addiction based on the stress response that could be used as an objective diagnostic method in the future.

Research Trend on the Use of IT in Digital Addiction: An Investigation Using a Systematic Literature Review

Despite the negative role of IT in digital addiction development, IT may have a positive role in dealing with digital addiction. The present study undertakes a systematic literature review to explore the state of play and the trend regarding the use of IT in digital addiction research. Using predefined keywords, the Scopus database was searched for relevant literature published from 2017 to 2020. The initial search found 1655 papers. Six stages of study selection were completed using a set of inclusion and exclusion criteria. The study selection and quality assessment process were applied, then 15 papers were selected for further review. The results show that addiction detection using IT is the most researched topic in digital addiction research. The most commonly used IT in the selected studies are AI methods and biosignal recording systems. Various approaches in detection, prevention, and intervention are suggested in the selected studies. The advantages and limitations of each approach are discussed. Based on these results, some future research directions are suggested.

Video Game Addiction and Emotional States: Possible Confusion Between Pleasure and Happiness?

Internet gaming disorder is characterized by a severely reduced control over gaming, resulting in an increasing gaming time and leading to negative consequences in many aspects of the individual life: personal, family, social, occupational and other relevant areas of functioning (World Health Organization). In the last years, the significant boom in using video games has been raising health issues that remain insufficiently understood. The extent of this phenomenon (the estimated prevalence is between 1.7 and 10% of the general population) has led the mentioned Organization to include gaming disorders in the list of mental health conditions (2018). Several studies show converging findings that highlight the common brain activities between substance use disorders and behavioral addictions (i.e., gaming disorders). Addiction specialists observed that addict subjects tend to confuse pleasure with happiness when linking emotional states to their addictive activities. As far as we know, beyond the mentioned observations, distinguishing the perception of these two emotional states in the frame of an addiction has not been yet the object of formal research. This study aims at examining the possible confusion between pleasure and happiness within the addiction sphere. Video game addiction has been chosen to explore the possible occurrence of this perceptional distortion. A mixed design lab-based study was carried out to compare between video games addicts and non-addicts (between-subjects), and video games-related activities and neutral activities (within-subject). Emotional reactions were gauged by self-reported scales and physiological data acquired through a range of biosensors: Relaxation and Hearth Rate. From a therapeutic standpoint, this research intends to explore alternatives to deal with this sort of disorders. More specifically, at the cognitive level, the idea is elaborating guidelines to develop patients' insights into these emotional states and thus increasing their ability to handle them. Overall, several indices resulting from this study constitute a bundle of arguments that argue in favor of the confusion between pleasure and happiness made by addict users when associating their affective states to video gaming. Furthermore, this approach illustrates how reappraising emotions may contribute to reducing the perceptional distortion of these emotional states.

Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use

Internet gaming disorder in adolescents and young adults has become an increasing public concern because of its high prevalence rate and potential risk of alteration of brain functions and organizations. Cue exposure therapy is designed for reducing or maintaining craving, a core factor of relapse of addiction, and is extensively employed in addiction treatment. In a previous study, we proposed a machine-learning-based method to detect craving for gaming using multimodal physiological signals including photoplethysmogram, galvanic skin response, and electrooculogram. Our previous study demonstrated that a craving for gaming could be detected with a fairly high accuracy; however, as the feature vectors for the machine-learning-based detection of the craving of a user were selected based on the physiological data of the user that were recorded on the same day, the effectiveness of the reuse of the machine learning model constructed during the previous experiments, without any further calibration sessions, was still questionable. This “high test-retest reliability” characteristic is of importance for the practical use of the craving detection system because the system needs to be repeatedly applied to the treatment processes as a tool to monitor the efficacy of the treatment. We presented short video clips of three addictive games to nine participants, during which various physiological signals were recorded. This experiment was repeated with different video clips on three different days. Initially, we investigated the test-retest reliability of 14 features used in a craving detection system by computing the intraclass correlation coefficient. Then, we classified whether each participant experienced a craving for gaming in the third experiment using various classifiers—the support vector machine, k-nearest neighbors (kNN), centroid displacement-based kNN, linear discriminant analysis, and random forest—trained with the physiological signals recorded during the first or second experiment. Consequently, the craving/non-craving states in the third experiment were classified with an accuracy that was comparable to that achieved using the data of the same day; thus, demonstrating a high test-retest reliability and the practicality of our craving detection method. In addition, the classification performance was further enhanced by using both datasets of the first and second experiments to train the classifiers, suggesting that an individually customized game craving detection system with high accuracy can be implemented by accumulating datasets recorded on different days under different experimental conditions.

The Development of the Turkish Craving for Internet Gaming Scale (CIGS): A Validation Study

As the use of digital technology has increased, abuse and addiction to technology have been identified among a minority of users. In the mid-1990s, the concept of internet addiction was first used. Today, almost every digital technology use has been claimed to have a minority of disordered users. One key aspect of addictive substance behaviors is craving. Craving is also an important component of behavioral addictions including digital technology disorders such as Internet Gaming Disorder. The aim of the present study was to develop the Turkish version of the Craving for Internet Gaming Scale (CIGS) via an adaptation of the Penn Alcohol Craving Scale (PACS). The present study comprised 368 adolescents from four different samples. The measures used included the Craving for Internet Gaming Scale, Digital Game Addiction Scale, and Brief Self-Control Scale. The structural validity of CIGS was investigated with Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and criterion validity. The reliability of CIGS was evaluated using Cronbach α internal consistency reliability coefficient and corrected item total correlation coefficients. As a result of EFA, it was found that the five-item CIGS had a single-factor structure. The unidimensional CIGS obtained as a result of EFA was tested with CFA. As a result of CFA, the unidimensional structure of CIGS was confirmed in two different samples. Criterion validity of CIGS was assessed via digital gaming addiction, self-discipline, impulsiveness, daily internet gaming duration, and internet gaming history. As a result of criterion analysis, CIGS was associated with these variables in the expected direction. Finally, according to reliability analysis, the CIGS was found to be a reliable scale. When validity and reliability analysis of the CIGS are considered as a whole, it is concluded that the CIGS is a valid and reliable scale that assesses craving for internet gaming.

A Novel Cue-Induced Abdominal Reaction Analysis for Internet Gaming Disorder

Individuals with Internet gaming disorder (IGD) frequently play online games to achieve satisfaction. Numerous signal processing questions regarding the negative consequences and characteristic respiration in a long-term sitting posture remain unanswered. This study recruited 50 individuals with high-risk and low-risk IGD (HIGD and LIGD); these participants were taught to perform a specific respiration during game-film stimuli. The instantaneous frequencies on abdominal movement (f_DF) were calculated with ensemble empirical mode decomposition (EEMD). The difference value (Δf_DF) between rest and stimulus statuses was calculated and found that HIGD showed Δf_DF values of 0.060 during positive stimuli and 0.055 during negative stimuli before the exercise but 0.020 and 0.016, respectively, after the exercise. However, the Δf_DF value for those with LIGD during negative stimuli before the exercise was 0.013, and it increased to 0.025 after the exercise. This is the first approach to IGD discrimination toward abdominal response with EEMD.