Altered cardiorespiratory coupling in young male adults with excessive online gaming

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.

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.

Identification of effective features of LFP signal for making closed-loop deep brain stimulation in parkinsonian rats

Traditional deep brain stimulation (DBS) is one of the acceptable methods to relieve the clinical symptoms of Parkinson's disease in its advanced stages. Today, the use of closed-loop DBS to increase stimulation efficiency and patient satisfaction is one of the most important issues under investigation. The present study was aimed to find local field potential (LFP) features of parkinsonian rats, which can determine the timing of stimulation with high accuracy. The LFP signals from rats were recorded in three groups of parkinsonian rat models receiving stimulation (stimulation), without getting stimulation (off-stimulation), and sham-controlled group. The frequency domain and chaotic features of signals were extracted for classifying three classes by support vector machine (SVM) and neural networks. The best combination of features was selected using the genetic algorithm (GA). Finally, the effective features were introduced to determine the on/off stimulation time, and the optimal stimulation parameters were identified. It was found that a combination of frequency domain and chaotic features with an accuracy of about 99% was able to determine the time the DBS must switch on. In about 80.67% of the 1861 different stimulation parameters, the brain was able to maintain its state for about 3 min after stimulation discontinuation.

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.

Instantaneous Respiratory Response of Gamer with High-Risk Internet Gaming Disorder during Game-Film Stimuli by Using Complementary Ensemble Empirical Mode Decomposition

Gamers with Internet gaming disorder (IGD) dynamically regulate their psychophysiological responses during playing; however, analyzing instantaneous psychophysiological responses in these gamers has been limited by a lack of appropriate methods. We propose combining the Complementary Ensemble Empirical Mode Decomposition and Direct Quadrature methods to overcome this limitation. The related effect of abdominal breathing (AB) training (as a relaxing psychology method) on the distribution of instantaneous frequency (IF) was investigated by calculating median (IF_med), kurtosis (IF_kurt) and skewness (IF_skew), and 19 participants with high-risk IGD (HIGD) were found to have increased IF_med [massively multiplayer online role-playing game (MMORPG): 0.36 ± 0.08; first-person shooter game (FPSG): 0.34 ± 0.08] but decreased IF_kurt (MMORPG: 5.98 ± 2.31; FPSG: 6.84 ± 4.61) and IF_skew (MMORPG: 0.40 ± 0.69; FPSG: 0.64 ±1.04) during game-film stimuli compared with baseline and recovery states. After AB training, IF_med of these 19 participants (MMORPG: 0.24 ± 0.11; FPSG: 0.18 ± 0.06) decreased significantly. This study is firstly to observe the IF distribution of respiratory signal in gamers with HIGD; thus, this distribution may be used as a respiratory physiological marker of IGD risk.

The Association between Excessive Internet Gaming Behavior and Immersive Tendency, Mediated by Attention Deficit/Hyperactivity Disorder Symptoms, in Korean Male University Students

OBJECTIVE

Problematic online gaming (POG) and problematic Internet use (PIU) have become a serious public mental health problem, with Internet gaming disorder (IGD) included in "Conditions for further study" section of DSM-5. Although higher immersive tendency is observed in people affected by POG, little is known about the simultaneous effect of immersive tendency and its highly comorbid mental disorder, attention deficit/hyperactivity disorder (ADHD). This study aimed to assess the relationship between immersive tendency, ADHD, and IGD.

METHODS

Cross-sectional interview study was conducted in Seoul, Korea with 51 male undergraduate students; 23 active gamers and 28 controls.

RESULTS

Current ADHD symptoms showed partial mediation effect on the path of immersive tendency on POG and PIU. The mediation model with inattention explained variance in both POG and PIU better than other current ADHD symptom models (R2=69.2 in POG; 69.3 in PIU). Childhood ADHD symptoms models demonstrated mediation effect on both POG and PIU which explained less variance than current ADHD symptom models (R2=53.7 in POG; 52.1 in PIU). Current ADHD symptoms, especially inattention, appear to mediate the effect of immersive tendency on POG/PIU.

CONCLUSION

Immersive tendencies may entail greater susceptibility to IGD, and comorbidity with ADHD may mediate the effect of immersive tendency on IGD.

Problematic Internet Use: The Relationship Between Resting Heart Rate Variability and Emotional Modulation of Inhibitory Control

A growing literature suggests that problematic Internet use (PIU) is associated with defective inhibitory control. In this study, we sought to investigate the ability to inhibit prepotent motor responses in an emotional context in individuals with PIU, and to examine the relationship between inhibitory control and resting heart rate variability (HRV), which is regarded as a measure of self-regulation and adaptability. Problematic users (PU, n = 20) and nonproblematic users (non-PU, n = 20) completed an emotional Go/NoGo task, involving the presentation of unpleasant, pleasant, and neutral pictures. The electrocardiogram was recorded at rest for a 3-minute period. PU showed lower resting HRV, relative to non-PU. Although reaction times (RTs) to Go task stimuli were not faster in PU, relative to non-PU, accuracy rates were significantly lower among PU, irrespective of pictures' emotional content. Only among PU did lower resting HRV predict lower response accuracy in pleasant and unpleasant Go trials and less efficient task performance (combining RTs to Go trials and accuracy to NoGo trials) upon presentation of unpleasant stimuli. Our findings suggest that reduced HRV is a potential indicator of defective inhibitory control in an emotional context in PIU.

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.

Exercise Science and Child Health: A Tale of Many Journeys

Children are the most naturally physically active human beings; reduced physical activity is a cardinal sign of childhood disease, and exercise testing provides mechanistic insights into health and disease that are often hidden when the child is at rest. The physical inactivity epidemic is leading to increased disease risk in children and, eventually, in adults in unprecedented ways. Cardiopulmonary exercise testing (CPET) biomarkers are used to assess disease severity, progress, and response to therapy across an expanding range of childhood diseases and conditions. There is mounting data that fitness in children tracks across the life span and may prove to be an early, modifiable indicator of cardiovascular disease risk later in life. Despite these factors, CPET has failed to fulfill its promise in child health research and clinical practice. A major barrier to more accurate and effective clinical use of CPET in children is that data analytics and testing protocols have failed to keep pace with enabling technologies and computing capacity. As a consequence, biomarkers of fitness and physical activity have yet to be widely incorporated into translational research and clinical practice in child health. In this review, the author re-examines some of the long-held assumptions that mold CPET in children. In particular, the author suggests that current testing strategies that rely predominantly on maximal exercise may, inadvertently, obfuscate novel and clinically useful insights that can be gleaned from more comprehensive data analytics. New pathways to discovery may emanate from the simple recognition that the physiological journey that human beings undertake in response to the challenge of exercise may be far more important than the elusive destination of maximal or peak effort.

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.

Cardiorespiratory Coordination in Repeated Maximal Exercise

Increases in cardiorespiratory coordination (CRC) after training with no differences in performance and physiological variables have recently been reported using a principal component analysis approach. However, no research has yet evaluated the short-term effects of exercise on CRC. The aim of this study was to delineate the behavior of CRC under different physiological initial conditions produced by repeated maximal exercises. Fifteen participants performed 2 consecutive graded and maximal cycling tests. Test 1 was performed without any previous exercise, and Test 2 6 min after Test 1. Both tests started at 0 W and the workload was increased by 25 W/min in males and 20 W/min in females, until they were not able to maintain the prescribed cycling frequency of 70 rpm for more than 5 consecutive seconds. A principal component (PC) analysis of selected cardiovascular and cardiorespiratory variables (expired fraction of O₂, expired fraction of CO₂, ventilation, systolic blood pressure, diastolic blood pressure, and heart rate) was performed to evaluate the CRC defined by the number of PCs in both tests. In order to quantify the degree of coordination, the information entropy was calculated and the eigenvalues of the first PC (PC1) were compared between tests. Although no significant differences were found between the tests with respect to the performed maximal workload (Wmax), maximal oxygen consumption (VO₂ max), or ventilatory threshold (VT), an increase in the number of PCs and/or a decrease of eigenvalues of PC₁ (t = 2.95; p = 0.01; d = 1.08) was found in Test 2 compared to Test 1. Moreover, entropy was significantly higher (Z = 2.33; p = 0.02; d = 1.43) in the last test. In conclusion, despite the fact that no significant differences were observed in the conventionally explored maximal performance and physiological variables (Wmax, VO₂ max, and VT) between tests, a reduction of CRC was observed in Test 2. These results emphasize the interest of CRC evaluation in the assessment and interpretation of cardiorespiratory exercise testing.