Identification of Video Game Addiction Using Heart-Rate Variability Parameters

Kim, Jung Yong; Kim, Hea Sol; Kim, Dong Joon; Im, Sung Kyun; Kim, Mi Sook

doi:10.3390/s21144683

“…Because the inspection of HRV involves noninvasive methods, those with substance use disorders could conduct self-assessments in their home environment [22]. HRV has been used as a biomarker for severe substance use disorder [18] and also as a useful biomarker for other addictions, such as video game addiction [23] and alcohol addiction. According to the results of this study, the biological HRV age may be more sensitive than traditional HRV parameters, such as LF and HF, to be a biomarker on addiction subjects.…”

Section: Discussionmentioning

confidence: 99%

Substance Misuse Decreases Heart Rate Variability

Lu

¹

,

Chou

²

,

Chang

³

et al. 2022

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There are few biomarkers to reflect the physiological conditions for substance misuse. In this study, autonomic nervous and cardiovascular parameters were measured for participants in deferred prosecution programs. The goal of this study is to develop significant biomarkers of the above subjects and enhance their motivation to join further substance misuse disorder treatment program. This study recruited 34 participants with a mean age of 35.1 years. The participants spent approximately five minutes filling in the basic information form and another five minutes filling in the Pittsburgh Sleep Quality Index (PSQI). Blood pressure, heart rate, and heart rate variability data were also measured. Their sleep quality was measured by the Pittsburgh Sleep Quality Inventory (PSQI) with an average score of 8.6 and average systolic and diastolic pressure measured of 116.1 ( standard deviation SD = 13.7 ) and 77.9 mmHg ( SD = 8.1 ) during resting, respectively. Average heartbeat rates are 90.4 beats per minute ( SD = 17.0 ); and average low-frequency power ratio is 56.4% ( SD = 19.8 %). Of the participants, 35% exhibited a low-frequency power rate below 30% and above 70%. The average biological HRV age of the participants was 46.0 years ( SD = 22.9 ), which was significantly older than their average actual age ( p = 0.002 ∗ ∗ ). The biological HRV age of the participants is 10 years older than their own actual age, and it effectively reflects the physiological conditions of participants in deferred prosecution programs. These physiological parameters, especially the biological HRV age, can be used in future treatment for substance use disorder counseling programs to increase the illness insight of program participants and increase their motivation to join further substance misuse disorder treatment program.

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“…Therefore, looking into the brain signal during the action of game playing was an inevitable step, despite the difficulty in controlling subjects during the experiment. Hafeez et al [ 12 ] and Kim et al [ 13 ] examined the bio-signals of IGD subjects during game playing and found significantly different EEG and ECG patterns in the IGD group compared to the healthy group. Moreover, the current study showed a 63.5%–73.1% accuracy in identifying the IGD subjects.…”

Section: Discussionmentioning

confidence: 99%

“…They reported an 86.9% (±9.49) detection rate in terms of the addicted group and an 88.0% (±10.41) detection rate in terms of the non-addicted group based on a θ parameter measurement at O2 (right occipital region). Kim et al [ 13 ] used heart rate variability (HRV) parameters to examine the patterns of players with gaming disorders during internet game playing. They used the IAT and CIUS tests to group the subjects.…”

Section: Introductionmentioning

confidence: 99%

EEG Parameter Selection Reflecting the Characteristics of Internet Gaming Disorder While Playing League of Legends

Kim

¹

,

Kim

²

,

Im

³

et al. 2023

Sensors

Self Cite

2

0

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Game playing is an accessible leisure activity. Recently, the World Health Organization officially included gaming disorder in the ICD-11, and studies using several bio-signals were conducted to quantitatively determine this. However, most EEG studies regarding internet gaming disorder (IGD) were conducted in the resting state, and the outcomes appeared to be too inconsistent to identify a general trend. Therefore, this study aimed to use a series of statistical processes with all the existing EEG parameters until the most effective ones to identify the difference between IGD subjects IGD and healthy subjects was determined. Thirty subjects were grouped into IGD (n = 15) and healthy (n = 15) subjects by using the Young’s internet addition test (IAT) and the compulsive internet use scale (CIUS). EEG data for 16 channels were collected while the subjects played League of Legends. For the exhaustive search of parameters, 240 parameters were tested in terms of t-test, factor analysis, Pearson correlation, and finally logistic regression analysis. After a series of statistical processes, the parameters from Alpha, sensory motor rhythm (SMR), and MidBeta ranging from the Fp1, C3, C4, and O1 channels were found to be best indicators of IGD symptoms. The accuracy of diagnosis was computed as 63.5–73.1% before cross-validation. The most interesting finding of the study was the dynamics of EEG relative power in the 10–20 Hz band. This EEG crossing phenomenon between IGD and healthy subjects may explain why previous research showed inconsistent outcomes. The outcome of this study could be the referential guide for further investigation to quantitatively assess IGD symptoms.

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“…Study sample sizes ranged from 23 to 148 participants, while group sizes varied from 11 to 50 individuals per group. Only male participants were enrolled in seven out of 11 studies [28][29][30][31][32][33][34]. While three studies enrolled adolescents as well-two with a defined age range from 15 to 25 years [31,33] and one from 16 to 29 years [32], the rest involved adult participants only.…”

Section: Research Participants and Study Designmentioning

confidence: 99%

Neurobiological Link between Stress and Gaming: A Scoping Review

Wang

¹

,

Simkute

²

,

Griškova-Bulanova

³

2023

JCM

0

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Research on video gaming has been challenged by the way to properly measure individual play experience as a continuum, and current research primarily focuses on persons with gaming disorder based on the diagnostic criteria established in relation to substance use and gambling. To better capture the complexity and dynamic experience of gaming, an understanding of brain functional changes related to gaming is necessary. Based on the proinflammatory hypothesis of addiction, this scoping review was aiming to (1) survey the literature published since 2012 to determine how data pertinent to the measurement of stress response had been reported in video gaming studies and (2) clarify the link between gaming and stress response. Eleven studies were included in this review, and the results suggest that gaming could stimulate a stress-like physiological response, and the direction of this response is influenced by an individual’s biological profile, history of gaming, and gaming content. Our findings highlight the need for future investigation of the stress-behaviour correlation in the context of gaming, and this will assist in understanding the biological mechanisms underlying game addiction and inform the potential targets for addiction-related proinflammatory research.

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Identification of Video Game Addiction Using Heart-Rate Variability Parameters

Cited by 10 publications

References 33 publications

Substance Misuse Decreases Heart Rate Variability

Substance Misuse Decreases Heart Rate Variability

EEG Parameter Selection Reflecting the Characteristics of Internet Gaming Disorder While Playing League of Legends

Neurobiological Link between Stress and Gaming: A Scoping Review

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