Prerpints.org logo
Preprint
Article

A 24h Change and Reactivity in Heart Rate Variability During Games May Predict Excessive Internet Gaming : Weak Top-Down Control Among Young Students

Altmetrics

Downloads

210

Views

59

Comments

0

Submitted:

29 March 2023

Posted:

03 April 2023

You are already at the latest version

Alerts
Abstract
Several studies have reported a relationship between autonomic dysfunction　 during tasks and Internet addiction. Changes within 24 h regarding heart rate variability (HRV) may be a more sensitive indicator than short term observations. We evaluated the influence of excessive Internet usage on HRV using a 24 h electrocardiogram (ECG) and examined acute changes during computer game tasks. A total 13 high school students and 22 young adults were recruited, and divided into excessive Internet user and non- excessive user groups based on Internet addiction test scores. The ECG was recorded continuously for 24 h by a wearable sensor and during two computer games (the Iowa gambling task and each participant’s preferred game) using a standard type ECG system. Multiple psychometric tests were also conducted. The excessive Internet user group exhibited clinically significant depressive symptoms, and higher self-reported scores for anxiety, impulsivity, autistic tendencies, and sleep problems than the non- excessive internet user group. There were weak differences within the 24 h high-frequency (HF) band power between two groups. During the Iowa gambling task, younger participants exhibited a reduced HF response, which may be related to weaker top- down self- regulatory mechanisms.
Keywords: 
Subject: Medicine and Pharmacology  -   Cardiac and Cardiovascular Systems

Introduction

Decreased HRV has been reported in various illnesses, including depression, psychotic disorders, and autism. For example, in autism, resting HRV is decreased, especially in relation to emotional control [1], and in schizophrenia and bipolar disorder, HRV has also been associated with severity of psychiatric symptoms [2].
Several studies have reported relationships between altered autonomic function and Internet addiction by examining heart rate variability (HRV) at rest or during a computer task [3,4,5,6,7,8,9] (Supplementary Table).
Heart rate variability reflects the balance between parasympathetic and sympathetic activity at the sinoatrial node, and the HRV spectrum can be divided into very-low, low (LF: 0.04-0.15 Hz), and high (HF: 0.15-0.40Hz) frequency bands that reflect different autonomic inputs. The HF component is considered to most strongly reflect vagus-mediated parasympathetic input [10]. However, the HF band is influenced by respiration and there is no broadly accepted method to remove this influence [11]. Alternatively, the [12]. LF band spectrum is thought to be mix of sympathetic and vagal activity, baroreflex activity. They highlight a link between cardiac sympathetic nerve activity and baroreflex sensitivity. However, traditional time and frequency domain measures fail to account for important properties related to multiscale organization and nonequilibrium dynamic. So the non-linear analysis based on entropy is recently investigated [13,14].
The HRV spectrum is computed directly from the variation in R-R (inter beat) intervals through statistical computation, which is sensitive to the length of the recording. Owing to the use of different acquisition and analysis procedures, it may be difficult to compare results across studies. In order to address this problem guidelines have been proposed [15,16].
Despite the methodological heterogeneity, such studies tend to show a weak relationship between HRV and Internet gaming addiction (Supplementary Table). Specific HRV indices may reflect the capacity of individuals to modulate cognitive activity, emotion, and behavior in response to changing environmental demands [17]. According to the perspective of Porges [18,19] and Thayer [20] HRV is a biomarker of top down regulation. Therefore, abnormalities in the HRV spectrum may predict deficits in cognitive, emotional, and (or) behavior control that predispose to Internet addiction.
Young people and students are considered more vulnerable to problematic Internet use [21,22].
While many studies have examined 24 hour HRV to evaluate cardiovascular neural regulation among health care workers [23], shift workers [24] and patients with coronary artery disease [25], such measures have not been conducted in healthy young adults with problematic Internet uses. The HF component displays a circadian pattern with peak power between 11 PM and 5 AM [26]. This circadian rhythm was altered in coronary artery disease patients [25] and night-shift workers [24]. Thus, long-term HRV analysis may be a more reliable tool for assessing autonomic dysfunction, as well as its relationship to disorders such as Internet addiction than, measurements during specific tasks [27]. But, the short-term HRV analysis is easy to perform and convenient to control the confounding factors.
In this study, we evaluated the differences in the HRV spectrum over 24 h and during computer tasks in males with or without excessive Internet gaming (EIG) behaviors. In addition, we compared the HRV spectrum between young adults and high-school students to assess the potential differences in behavioral control with maturity.

Methods and Materials

Participants

All the participants were recruited through postings in the Hirosaki Universities’ campus Internet. This study cohort consisted of male individuals aged 15-35 years and included high-school students (13) and young adults (22). The participants were classified into the EIG group and non-EIG group using Young’s Internet Addiction Test (IAT) [28]. A monetary reward of approximately USD 40 was given to each participant for their time and effort. The author screened the participants using a standard clinical assessment battery consisting of a psychiatric evaluation, structured diagnostic interview (the Structured Clinical Interview for DSM – IV) [29], and a medical history review. The study protocol was approved by the Research Ethics Committees of Hirosaki Universities, and the participants provided full written informed consent for their participation in the study.

Measures

Autonomic measures

Electrocardiograms (ECGs) were recorded continuously for 24 h with a wearable sensor- type ECG (Imec, Leuven, Belgium) and during game play (Iowa gambling task and each participant’s own game) using a standard- type ECG system. Electrophysiological signals were sampled at 256Hz from the wearable- type ECG and at 200Hz from the standard- type ECG, and saved for offline analysis. The R – peaks in the ECG waveform were detected using an automated detection algorithm and subsequently verified by visual inspection using Hyper Wave 2.1 (Kissei Comtec, Nagano, Japan). A power spectral analysis of the beat to beat R-R intervals time series was performed using MemCalc software (GMS, Nagano, Japan). For frequency domain analyses, the recording period was divided into small segments of continuous 5 minute periods. For each segment, corresponding data points per segment were transferred to power spectral density by the maximum entropy method[30].
Both LF power (0.04 - 0.15Hz) and HF power (0.15 - 0.40 Hz) were measured. Data from each band were transformed into natural logarithms, and LF / HF ratios were calculated. The HRV values for the initial rest period and the two games were averaged. For the 24 h ECG analysis, we defined the HF difference as the maximum HF (60 min) / minimum HF (60 min) (Fig. 1). The 24 hour mean values for HF, LF, and LF/HF were averaged over 24 hours.
We also calculated average standard deviation of normal to normal inter-beat interval (SDNN) and square root of the mean of squared differences between successive beat intervals (RMSSD). SDNN is considered to reflect both sympathetic and parasympathetic activities, and RMSSD has relevance to parasympathetic functioning [31].

Questionnaires

The IAT developed by Young et al. [28] consists of 20 items, and each item is scaled by five points. The total IAT score ranges from 20 to 100, and a total score over 40 signifies problems caused by Internet use. Clinical psychiatric ratings were assessed using the Beck Depression Inventory [32,33], State Trait Anxiety Inventory [34,35], and Autism Spectrum Quotient [36,37].

Procedures

The participants were instructed not to smoke, drink alcohol or consume caffeinated beverages the night before data collection. In order to record the ECG, three disposable Ag/AgCl electrodes were placed on the participant’s chest.
After a rest period of at least 5 min, the participants played two games in succession, namely the Iowa gambling task (IGT) and one of their own choice played daily, with a 4 min rest between each game session. A computer version of the IGT [38] was used. The subjects were instructed to select 100 cards arranged in four decks with the intention of gaining simulated monetary rewards. Each card had a monetary value that was revealed only after selection, and could either be a gain or a loss. The aim of the task was to optimize net gains across several trials. Two decks had higher rewards (gains) but also higher risks (losses) and could result in monetary losses over time, while the other two decks had lower rewards and even lower risks, making them advantageous in the long term. The IGT is a clinically sensitive tool to evaluate problems in impulse control and emotion regulation. The subjects used a mouse to select cards from one of the four decks and the amounts won or lost per trial as well as the total cumulative earnings were recorded. After the participants finished the games, a wearable sensor- type ECG was attached to their anterior chest wall for 24 h recordings.

Statistical analysis

The normality of the data distribution was assessed using the Shapiro-Wilk test. The comparison between the EIG and non - EIG groups, and between the adult and high-school student groups conformed to the normal distribution using the T test. The Mann–Whitney U test was used if the comparison which didn’t conform to the normal distribution
The numbers of cards selected in each block of the IGT were compared by two- way repeated measures analysis of variance between the EIG and non- EIG groups, and between the adult and high-school student groups. All p- values were two- tailed, and p < 0.05 was defined as statistically significant. The statistical analysis was conducted using SPSS version 23.0 for Windows (SPSS, Chicago, IL, USA).

Results

Clinical characteristics of the subjects

EIG vs. non - EIG

The subjects with an EIG also exhibited clinically significant depressive symptoms, and significantly higher self-reported scale scores for anxiety, and autism tendency, and sleep problem. However, only 24 hours averaged HF demonstrated significant group differences in terms of HRV indices (Table 2).
Table 1. Demographics, clinical variables of subjects and absolute values of HRV parameters in excessive Internet user and non – excessive user groups　.
Table 1. Demographics, clinical variables of subjects and absolute values of HRV parameters in excessive Internet user and non – excessive user groups　.
Preprints 70313 i001
BDI-II; Beck depression inventory, STAI; state and trait of anxiety; IGT; Iowa gambling scale, HF; high frequency, LF; low frequency, SDNN; standard deviation of N-N intervals, RMSDD; root-mean-squared differences N-N intervals; ＊； p<0.05　　†; Mann-Whitney tes.t
Figure 1.
High-school student vs. young adult.
The 24 h HF power displayed no difference, but the adult group exhibited greater HF power during the IGT while the high-school student group exhibited reduced HF power compared to the baseline. There were no differences in clinical symptoms between age groups except for the IGT score and body mass index (Table 3).

Effects of EIG and age on IGT scores

In the EIG vs. non-EIG comparison, there was no significant group x block interaction (F = 0.474, df = 4, p = 0.755), or main effect of the group (F = 0.429, df = 1, p = 0.517) on the IGT score, but there was a significant main effect of the block (F = 6.372, df = 4, p = 0.00) (Fig. 2).
Figure 2.
In the age group comparison, there was also no significant group x block interaction (F = 1.853, df = 4, p = 0.122), but there was a main effect of the group (F = 5.168, df = 1, p = 0.030) and a significant main effect of the block (F = 4.425, df = 4, p = 0.002) (Fig. 3).
Figure 3.

Discussion

To the best of our knowledge, this is the first study to examine autonomic changes in young males with and without excessive Internet gaming behaviors using 24 h HRV monitoring. While there was no difference in the HRV band power change (min-max) between the EIG and non –EIG groups, we found a significant difference in the daily average of HF. On the other hand, there was no significant difference in HF during IGT or when the subject was playing games that he played every day.
The results of the previous reports on HF at rest showed that HF decreased when depression [39] or Internet dependence were strong [3,4,5,6,7,8,9], which was the opposite of the present results. The subjects in this study did not demonstrate the severity of Internet addiction observed in previous studies [3,4,5,6,7,8,9]. The present study was conducted on healthy subjects classified by IAT, and the low level of severity was considered to be a reason for which no significant differences were found except for HF. HRV is related to frontal lobe function, and moderate game use is reported to have a positive effect on it [40].
According to Tateno et al. [41], an IAT cut- off point of 40 is not appropriate to evaluate Internet addiction, while 50 may be ideal.
In addition, the 24 hour measurement was insufficient, including sleep and various daily activities. Strictly speaking, it is necessary to conduct more detailed studies on the differences in the amount of exercise, sleep stage, etc., by using accelerometers and PSG together.
The clinical usefulness of Heart rate variability is not yet well understood, whether short-time or 24-hour measurements are better. For example, in myocardial infarction Lu et al. [42] demonstrated that SDNN of a 5-min ECG recording could predict the mortality of patients with myocardial infarction, but was inferior to long- term HRV indices. In contrast, Bigger et al. [43] compared the power spectrum of short ECG recordings with 24 h long-term HRV, and concluded that both HRV analyses were excellent predictors of mortality in patients with myocardial infarction.
There was a significant difference in the depressive state, anxiety, impulsivity, autistic tendency, and sleep problems between both groups; thus, at the early stage of excessive Internet gaming, psychometric testing appears superior to HRV monitoring for evaluating the psychological factors contributing to this aberrant behavior. Using multimodal biosignals such as EEG and galvanic skin response [44], as well as other kinds of tests such as an individual minute test, which evaluates time perception, will improve the detection of Internet gaming addiction [45]. In the past, gaming, social tasks and watching films have been used to evaluate heart rate variability reactivity (Table 1), but there was not significant result compared to the baseline heart rate variability [2].
Hong et al. [7] also observed significant reductions in HF power during periods of high attention and during the last 5 min of the task. It is possible that the time course of the game may influence HRV. However, we did not find time course changes between both groups in terms of HF during the IGT.
When the overall results were classified by age, there were significant differences in the IGT scores and responses to IGT between the high-school students and the adults. In other words, the IGT scores were significantly higher in adults than in high-school students, and the responses to HF were greater in adults than in high school students. This is consistent with the authors of [46], who found that adolescents have a poor decision-making process and lower IGT results. It is also consistent with the results found in [47] according to which the better the IGT results, the more autonomic activity is activated. In Table 3, there was a significant difference in BMI between both groups, but Antelmi et al. [48] found no significant difference in terms of HRV between the normal, overweight, and obese groups of men and women.
Therefore, it is reasonable to limit the time and content of Internet use for high school students who are in the process of maturing their decision-making process, as has been conventionally established.
According to a recent review, there is a small but significant relationship between HRV and top-down self- regulation, and this relationship is thought to be stronger in older individuals [17]. The observed differences in terms of HRV in our study between age groups suggest such a relationship.
This study has several limitations. First, excessive Internet use was less severe than in previous studies, so relationships between HRV indices and EIG are still possible. Second, we did not monitor respiratory changes during game playing, and differences in respiratory response could influence the HF band of the HRV spectrum. However, a recent study found no significant group difference in respiration during a gaming task [4]. Third, we did not consider specific details of the participants’ Internet use such as excessive social networking and online shopping, gambling, or pornography use.
In conclusion, the 24 hour average HF of the wearable sensor demonstrated a significant difference between the EIG and non -EIG groups. Contrary to previous results, however, the averaged HF was higher in the EIG group, which was more likely to be depressed and to have pre-Internet tendencies. This result was an evaluation of net-dependent tendencies in healthy subjects, and it is possible that a moderate use of the net may have resulted in functional improvement.
Younger participants also tended to exhibit a decreased HF response during the IGT, which may be related to weakness in top down self regulatory mechanisms compared to older individuals.
In this study, we performed long-time measurement for 24 hours and short-time loading for a few minutes, and found significant differences only in the long-time frequency analysis. In the future, in addition to the parameters to be compared in the analysis method, it is necessary to solve such problems as what kind of load is likely to cause differences in short time, and whether the long time measurement should be divided into resting and sleeping periods.

Supplementary Materials

The following supporting information can be downloaded at the website of this paper posted on Preprints.org.

Author Contributions

Y.O. designed the study, collected and analyzed the data, and wrote the draft. M.K. and K.N. contributed to data collection, and M.O. contributed to HRV data analysis. M.K. and M.O. provided critical revision of the manuscript and important intellectual content.

Funding

This research was supported by the Japan Internet Safety Promotion. Association. This research is partially supported by the Center of Innovation Program from the Japan Science and Technology Agency.

Institutional Review Board Statement

This study was conducted according to the guidelines of the Declaration of Helsinki, and was approved by the Research Ethics Committee of the University of Hirosaki (protocol code: 2021-122).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are available upon request.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Benjamin BR, Valstad M, Elvsåshagen T, Jönsson EG, Moberget T, Winterton A, Haram M, Høegh MC, Lagerberg TV, Steen NE, Larsen L, Andreassen OA, Westlye LT, Quintana DS. Heart rate variability is associated with disease severity in psychosis spectrum disorders. Progress in neuro-psychopharmacology & biological psychiatry, 2021, 111, 110108. https://doi.org/10.1016/j.pnpbp.2020.110108. [CrossRef]
  2. Chiu HT, Ip IN, Ching FNY, Wong BP, Lui WH, Tse CS, Wong SWH. Resting Heart Rate Variability and Emotion Dysregulation in Adolescents with Autism Spectrum Disorder. J Autism Dev Disord. 202310.1007/s10803-022-05847-x. Advance online publication. https://doi.org/10.1007/s10803-022-05847-x. [CrossRef]
  3. Lin, P. C., Kuo, S. Y., Lee, P. H., Sheen, T. C., & Chen, S. R. Effects of internet addiction on heart rate variability in school-aged children. Journal of Cardiovascular Nursing. 2014; 29: 493-498. https://doi.org/10.1097/JCN.0b013e3182a477d5. [CrossRef]
  4. Chang, J. S., Kim, E. Y., Jung, D., et al. Altered cardiorespiratory coupling in young male adults with excessive online gaming. Biological Psychology. 2015; 110; 159-166. https://doi.org/10.1016/j.biopsycho.2015.07.016. [CrossRef]
  5. Hsieh, D. L., Hsiao, T. C. Respiratory sinus arrhythmia reactivity of internet addiction abusers in negative and positive emotional states using film clips stimulation. BioMedical Engineering Online. 2016 15(1), 69. https://doi.org/10.1186/s12938-016-0201-2. [CrossRef]
  6. Kim, N., Hughes, T. L., Park, C. G., Quinn, L., Kong, I. D.. Altered Autonomic Functions and Distressed Personality Traits in Male Adolescents with Internet Gaming Addiction. Cyberpsychology, Behavior, and Social Networking. 2016; 19: 667-673. https://doi.org/10.1089/cyber.2016.0282. [CrossRef]
  7. Hong, S. J., Lee, D., Park, J., et al. Altered heart rate variability during gameplay in internet gaming disorder: The impact of situations during the game. Frontiers in Psychiatry. 2018 　9, 429.　https://doi.org/10.3389/fpsyt.2018.00429. [CrossRef]
  8. Moretta, T., Buodo, G. Autonomic stress reactivity and craving in individuals with problematic Internet use. PLoS ONE. 2018 13(1), e0190951. https://doi.org/10.1371/journal.pone.0190951. [CrossRef]
  9. Lee D, Hong SJ, Jung YC, Park J, Kim IY, Namkoong K. Altered Heart Rate Variability During Gaming in Internet Gaming Disorder. Cyberpsychol Behav Soc Netw. 2018 Apr;21(4):259-267. https://doi.org/10.1089/cyber.2017.0486. [CrossRef]
  10. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Eur Heart J 1996, 17(3): 354-381.
  11. Widjaja, D., A. Caicedo, E. Vlemincx, I. Van Diest, S. Van Huffel . Separation of respiratory influences from the tachogram: a methodological evaluation. PLoS One, 2014 9(7): e101713. https://doi.org/10.1371/journal.pone.0101713. [CrossRef]
  12. Berntson, G. G., Bigger, J. T., Eckberg, D. L., et al. HRV_Orgins, methods, and interpretive caveats. Psychophysiology. 1997; 34: 623-648. https://doi.org/10.1111/j.1469-8986.1997.tb02140.x. [CrossRef]
  13. Martelli D, Silvani A, McAllen RM, May CN, Ramchandra R. The low frequency power of heart rate variability is neither a measure of cardiac sympathetic tone nor of baroreflex sensitivity. Am J Physiol Heart Circ Physiol. 2014 Oct 1;307(7):H1005-12. https://doi.org/10.1152/ajpheart.00361.2014. [CrossRef]
  14. Abellán-Aynés, O., Naranjo-Orellana, J., Manonelles, P., & Alacid, F. MULTISCALE ENTROPY AND MULTISCALE TIME IRREVERSIBILITY ANALYSIS OF RR TIME SERIES DEPENDING ON AMBIENT TEMPERATURE. Journal of Mechanics in Medicine and Biology, 2020. 20, 2050029. https://doi.org/10.1142/S0219519420500293. [CrossRef]
  15. Quintana, D. S., Alvares, G. A., & Heathers, J. A. J. Guidelines for Reporting Articles on Psychiatry and Heart rate variability (GRAPH): recommendations to advance research communication. In Translational psychiatry. 2008; 43: 895-909.
  16. Laborde, S., Mosley, E., Thayer, J. F. Heart rate variability and cardiac vagal tone in psychophysiological research - Recommendations for experiment planning, data analysis, and data reporting. Frontiers in psychology, 8, 213. https://doi.org/10.3389/fpsyg.2017.00213. [CrossRef]
  17. Holzman, J. B., & Bridgett, D. J. Heart rate variability indices as bio-markers of top-down self-regulatory mechanisms: A meta-analytic review. Neuroscience and Biobehavioral Reviews. 2017; 74: 233-255. https://doi.org/10.1016/j.neubiorev.2016.12.032. [CrossRef]
  18. Porges, S. W. The polyvagal theory: phylogenetic substrates of a social nervous system　Int. J. Psychophysiol. 2001; 42:123-146. https://doi.org/10.1016/S0167-8760(01)00162-3. [CrossRef]
  19. Porges, S. W. A phylogenetic journey through the vague and ambiguous Xth cranial nerve: a commentary on contemporary heart rate variability research. Biol Psychol 2007; 74(2): 301-307. https://doi.org/10.1016/j.biopsycho.2006.08.007. [CrossRef]
  20. Thayer, J. F. R. D. Lane. Claude Bernard and the heart-brain connection: further elaboration of a model of neurovisceral integration. Neurosci Biobehav Rev 2009; 33: 81-88. https://doi.org/10.1016/j.neubiorev.2008.08.004. [CrossRef]
  21. Bakken, I. J., Wenzel, H. G., Götestam, K. G., Johansson, A., Øren, Internet addiction among Norwegian adults: A stratified probability sample study. Scandinavian Journal of Psychology. 2009; 50:121-127. https://doi.org/10.1111/j.1467-9450.2008.00685.x. [CrossRef]
  22. Kuss, D. J., Van Rooij, A. J., Shorter, G. W., Griffiths, M. D., Van De Mheen, D. Internet addiction in adolescents: Prevalence and risk factors. Computers in Human Behavior. 2013; 29: 1987-1996. https://doi.org/10.1016/j.chb.2013.04.002. [CrossRef]
  23. Goffeng, E. M., Nordby, K. C., Tarvainen, M. P.,et al. Fluctuations in heart rate variability of health care workers during four consecutive extended work shifts and recovery during rest and sleep. Industrial Health. 2018; 56: 122-131. https://doi.org/10.2486/indhealth.2017-0100. [CrossRef]
  24. Borchini, R., Ferrario, M. M., Bertù, L., et al. Prolonged job strain reduces time-domain heart rate variability on both working and resting days among cardiovascular-susceptible nurses. International Journal of Occupational Medicine and Environmental Health. 2015; 28: 42-51. https://doi.org/10.2478/s13382-014-0289-1. [CrossRef]
  25. Huikuri, H. V., Niemelä, M. J., Ojala, S., Rantala, A., Ikäheimo, M. J., & Airaksinen, K. E. J. Circadian rhythms of frequency domain measures of heart rate variability in healthy subjects and patients with coronary artery disease: Effects of arousal and upright posture. Circulation. 1994; 90: 121-126. https://doi.org/10.1161/01.cir.90.1.121. [CrossRef]
  26. Bilan, A., Witczak, A., Palusiński, R., Myśliński, W., Hanzlik, J. Circadian rhythm of spectral indices of heart rate variability in healthy subjects. Journal of Electrocardiology. 2005; 38: 239-243. https://doi.org/10.1016/j.jelectrocard.2005.01.012. [CrossRef]
  27. Li, K., H. Rudiger, T. Ziemssen. Spectral Analysis of Heart Rate Variability: Time Window Matters. Front Neurol 2019; 10: 545. https://doi.org/10.3389/fneur.2019.00545. [CrossRef]
  28. Young KS. Caught in the Net: How to Recognize the Signs of Internet Addiction – and a Winning Strategy for Recovery. Wiley, New York.
  29. American Psychiatric Association. Diagnostic and statistical manual of mental disorders Text Revision (DSM-IV-TR) , American Psychiatric Association, Washington, D.C, 2000.
  30. Sawada Y, Ohtomo N, Tanaka Y, Tanaka G, Yamakoshi K, Terachi S, Shimamoto K, Nakagawa M, Satoh S, Kuroda S, Iimura O. New technique for time series analysis combining the maximum entropy method and non-linear least squares method: its value in heart rate variability analysis. Med Biol Eng Comput. 1997 Jul;35(4):318-22. https://doi.org/10.1007/BF02534083. [CrossRef]
  31. Shaffer F, Ginsberg JP. An Overview of Heart Rate Variability Metrics and Norms. Front Public Health. 2017 5, 258.https://doi.org/10.3389/fpubh.2017.00258. [CrossRef]
  32. Beck, A. T., Steer, R. A., Brown, G. K. Manual for the Beck depression inventory-II. San Antonio, TX: Psychological Corporation. 1996.
  33. Kojima, M., Furukawa, T. A., Takahashi, H., Kawai, M., Nagaya, T., Tokudome, S. Cross-cultural validation of the Beck Depression Inventory-II in Japan. Psychiatry Research. 2002; 31: 291-299. https://doi.org/10.1016/s0165-1781(02)00106-3. [CrossRef]
  34. Spielberger, C. D., Reheiser, E. C. Assessment of Emotions: Anxiety, Anger, Depression, and Curiosity. Applied Psychology: Health and Well-Being. 2009; 15: 271-302. https://doi.org/10.1111/j.1758-0854.2009.01017.x. [CrossRef]
  35. Nakazato K, Mizuguchi T. Development and validation of Japanese version of state-trait anxiety inventory: a study with female subjects. hinshin Igaku [in Japanese]. 1982;22(s):107–12.
  36. Wakabayashi A, Tojo Y, Baron-Cohen S, Wheelwright S. The autismSpectrum quotient (AQ) Japanese version: evidence from high-functioning clinical group and normal adults. Shinrigaku Kenkyu. 2004;75 : 8–84. https://doi.org/10.4992/jjpsy.75.78. [CrossRef]
  37. Woodbury-Smith, M. R., Robinson, J., Wheelwright, S., Baron-Cohen, S. Screening adults for Asperger Syndrome using the AQ: A preliminary study of its diagnostic validity in clinical practice. Journal of Autism and Developmental Disorders. 2005: 35; 331-335. https://doi.org/10.1007/s10803-005-3300-7. [CrossRef]
  38. Bechara A., Damasio A. R., Damasio H., Anderson S. W. Insensitivity to future consequences following damage to human prefrontal cortex. Cognition,1994; 50 : 7–15. https://doi.org/10.1016/0010-0277(94)90018-3. [CrossRef]
  39. Blood JD, Wu J, Chaplin TM, Hommer R, Vazquez L, Rutherford HJ, Mayes LC, Crowley MJ. The variable heart: High frequency and very low frequency correlates of depressive symptoms in children and adolescents. J Affect Disord. 2015 Nov 1;186:119-26. https://doi.org/10.1016/j.jad.2015.06.057. [CrossRef]
  40. Smirni D, Garufo E, Di Falco L, Lavanco G. The Playing Brain. The Impact of Video Games on Cognition and Behavior in Pediatric Age at the Time of Lockdown: A Systematic Review. Pediatr Rep. 2021 Jul 14;13(3):401-415. https://doi.org/10.3390/pediatric13030047. [CrossRef]
  41. Tateno, M., Teo, A. R., Shiraishi, M., Tayama, M., Kawanishi, C., Kato, T. A. Prevalence rate of Internet addiction among Japanese college students: Two cross-sectional studies and reconsideration of cut-off points of Young’s Internet Addiction Test in Japan. Psychiatry and Clinical Neurosciences. 2018; 72: 723-730. https://doi.org/10.1111/pcn.12686. [CrossRef]
  42. Fei, L., Copie, X., Malik, M., & Camm, A. J. Short- and long-term assessment of heart rate variability for risk stratification after acute myocardial infarction. The American journal of cardiology, 1996.77(9),681–684. https://doi.org/10.1016/s0002-9149(97)89199-0. [CrossRef]
  43. Bigger, J. T., Jr, Steinman, R. C., Rolnitzky, L. M., Fleiss, J. L., Albrecht, P., & Cohen, R. J. Power law behavior of RR-interval variability in healthy middle-aged persons, patients with recent acute myocardial infarction, and patients with heart transplants. Circulation, 1996　93(12), 2142–2151. https://doi.org/10.1161/01.cir.93.12.2142. [CrossRef]
  44. Kim H, Kim L, Im CH. Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use. Sensors (Basel). 2019 Aug 9;19(16):3475. https://doi.org/10.3390/s19163475. [CrossRef]
  45. Krivonogova O, Krivonogova E, Poskotinova L. Time Estimation or Autonomic Heart Rate Regulation: Which Mechanism Is More Sensitive in the Development of Internet Addiction in Adolescents? Int J Environ Res Public Health. 2022 Sep 22;19(19):11977. https://doi.org/10.3390/ijerph191911977. [CrossRef]
  46. Beitz KM, Salthouse TA, Davis HP. Performance on the Iowa Gambling Task: From 5 to 89 years of age. J Exp Psychol Gen. 2014 Aug;143(4):1677-1689. https://doi.org/10.1037/a0035823. [CrossRef]
  47. Forte G, Morelli M, Casagrande M. Heart Rate Variability and Decision-Making: Autonomic Responses in Making Decisions. Brain Sci. 2021 Feb 15;11(2):243. https://doi.org/10.3390/brainsci11020243. [CrossRef]
  48. Antelmi I, de Paula RS, Shinzato AR, Peres CA, Mansur AJ, Grupi CJ. Influence of age, gender, body mass index, and functional capacity on heart rate variability in a cohort of subjects without heart disease. Am J Cardiol. 2004 Feb 1;93(3):381-5. https://doi.org/10.1016/j.amjcard.2003.09.065. [CrossRef]
Preprints 70313 g001
Figure 1. Time course of the heart rate variability (HRV) high- frequency (HF) power band in a young adult male.
Figure 1. Time course of the heart rate variability (HRV) high- frequency (HF) power band in a young adult male.
Preprints 70313 g001
Preprints 70313 g002
Figure 2. Performance on each block of the Iowa gambling task (IGT) for non-excessive Internet gaming (non-EIG) user and excessive Internet gaming (EIG) user groups. Each net score (1- 5) represents the results of 20 sequential card selections. Net score was calculated by subtracting the number of disadvantageous deck selections (losses) from the number of advantageous card selections (gains).
Figure 2. Performance on each block of the Iowa gambling task (IGT) for non-excessive Internet gaming (non-EIG) user and excessive Internet gaming (EIG) user groups. Each net score (1- 5) represents the results of 20 sequential card selections. Net score was calculated by subtracting the number of disadvantageous deck selections (losses) from the number of advantageous card selections (gains).
Preprints 70313 g002
Preprints 70313 g003
Figure 3. Performance on each Iowa gambling task block for high school students and adults.
Figure 3. Performance on each Iowa gambling task block for high school students and adults.
Preprints 70313 g003
Table 2. Demographics, clinical variables of subjects and absolute values of HRV parameters in adult and high school student groups.
Table 2. Demographics, clinical variables of subjects and absolute values of HRV parameters in adult and high school student groups.
Preprints 70313 i002
BDI-II; Beck depression inventory, STAI; state and trait of anxiety, IGT; Iowa gambling scale, HF; high frequency, LF; low frequency, SDNN; standard deviation of N-N intervals, RMSDD; root-mean-squared differences N-N intervals ＊； p<0.05　†; Mann-Whitney test.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Prerpints.org logo

Preprints.org is a free preprint server supported by MDPI in Basel, Switzerland.

facebook logotwitter logolinkedin logo
MDPI Initiatives
Important Links
Subscribe

Disclaimer

Terms of Use

Privacy Policy

© 2024 MDPI (Basel, Switzerland) unless otherwise stated