Effectiveness of Short-Term Heart Rate Variability Biofeedback Training and the Risk of Internet Addiction in Adolescents 15-16 Years of Age

Liliya V. Poskotinova, Olga V. Krivonogova, Oleg S. Zaborsky

 
International Journal of Biomedicine. 2020;10(2):153-156.
DOI: 10.21103/Article10(2)_OA13
Originally published June 15, 2020.

Abstract: 

Background: Adolescents with an Internet overuse problem and risk of Internet addiction (IA) have a disturbed autonomic nervous system balance. The aim of the study was to determine the effectiveness of short-term heart rate variability biofeedback (HRV-BF) training to increase the total power (TP) of HRV spectrum in adolescents 15-16 years of age with different risks of IA development.
Materials and Results: The study involved 20 healthy youths (15-16 years of age) of Arkhangelsk secondary school. The survey was conducted using the Chen Internet Addiction Scale (CIAS) in the Russian version of Malygin et al.(2011).
SBP (systolic blood pressure), DBP (diastolic blood pressure) and HRV indicators (HR, TP of the HRV spectrum, and SI) were recorded in relaxation (3 min) and during the HRV-BF training session (3 min). According to the CIAS score, 2 groups were identified: Group 1 (n=9) with minimal IA risk (CIAS score <47) and Group 2 (n=11) with significant IA risk (CIAS score ≥47 points). Group 1, after HRV-BF training, showed a significant increase in TP compared to the initial value, on average by 2.3 times (P=0.036). At the same time, SI decreased significantly (P=0.025). In Group 2, after HRV-BF training we did not find significant change in TP and SI, compared to the initial data. Moreover, HR became statistically higher (P=0.021). TP level after HRV-BF training in Group I was significantly higher than in Group 2 (P=0.043). SBP and DBP did not statistically change during the training in both groups. Correlation analysis performed on the total sample (n=20) revealed a significant negative correlation between high TP levels during HRV-BF training and low CIAS scores on the Wit-scale (rS =-0.46, P=0.048).
Conclusion: A significant risk of IA developing in puberty may be accompanied by a decrease in the autonomic nervous reactivity during the HRV-BF session. The greatest influence on reduction of HRV-BF efficiency during short-term training has withdrawal symptoms associated with excessive Internet use.

Keywords: 
Internet addiction • adolescents • heart rate variability biofeedback
References: 
  1. Cerniglia L, Zoratto F, Cimino S, Laviola G, Ammanti M, Adriani W. Internet Addiction in adolescence: Neurobiological, psychosocial and clinical issues. Neurosci Biobehav Rev. 2017;76(Pt A):174-184. doi: 10.1016/j.neubiorev.2016.12.024.
  2. Chen S, Weng L, Su Y, Wu H, Yang P. Development of a Chinese Internet addiction scale and its psychometric study. Chinese Journal of Psychology. 2003.45:279–294.
  3. Kwok-Kei Mak, Ching-Man Lai, Chih-Hung Ko, Chien Chou, Dong-Il Kim, Hiroko Watanabe, Roger C M Ho. Psychometric properties of the revised Chen Internet Addiction Scale (CIAS-R) in Chinese adolescents. J Abnorm Child Psychol. 2014;42(7):1237–45. doi: 10.1007/s10802-014-9851-3.
  4. Kim N, Hughes TL, Park CG, Quinn L, Kong ID. Altered Autonomic Functions and Distressed Personality Traits in Male Adolescents with Internet Gaming Addiction. Cyberpsychol Behav Soc Netw. 2016;19(11):667‐673. doi:10.1089/cyber.2016.0282
  5. Moretta T, Buodo G. Autonomic stress reactivity and craving in individuals with problematic Internet use. PLoS One. 2018;13(1):e0190951. Published 2018 Jan 16. doi:10.1371/journal.pone.0190951
  6. Lehrer PM, Gevirtz R. Heart rate variability biofeedback: how and why does it work? Front Psychol. 2014;5:756. doi: 10.3389/fpsyg.2014.00756.
  7. Poskotinova LV, Demin DB, Krivonogova EV. Short-term HRV Biofeedback: Perspectives in Environmental Physiology and Medicine. International Journal of Biomedicine. 2017;7(1):24-27.  doi: 10.21103/Article7(1)_RA3
  8. Order № 514n Ministry Of Health Russia [«O Poryadke provedeniya profilakticheskikh meditsinskikh osmotrov nesovershennoletnikh»], date 2017 August 10. [Article in Russian]. [cited 2020 April 20]. Available from: http://base.garant.ru/71748018
  9. WHO. Growth reference data for 5-19. Available from: https://www.who.int/growthref/en/
  10. Malygin VL, Feklysov KA, Iskandirova AB, Antonenko AA. [Methodological approaches to the early detection of Internet-dependent behavior]. [Article in Russian ]. [Electronic source]. Medical Psychology in Russia: an electronic scientific journal. 2011;6. Available from: http://medpsy.ru/mprj/archiv_global/2011_6_11/nomer/nomer03.php
  11. Ko CH, Yen JY, Yen CF, Chen CC, Yen CN, Chen SH. Screening for Internet addiction: an empirical study on cut-off points for the Chen Internet Addiction Scale. Kaohsiung J Med Sci. 2005;21(12):545‐551. doi:10.1016/S1607-551X(09)70206-2
  12. Patent RUS № 2317771. Poskotinova LV, Semenov YuN. Method for correcting vegetative misbalance states with Varicard complex for processing cardiointervalograms and analyzing cardiac rhythm variability, operating under computer software program with biofeedback. [Abstract of invention in Russian]. Available from:  https://patentimages.storage.googleapis.com/2a/b4/c7/68fcfa35cce6e1/RU23...
  13. Poskotinova LV, Krivonogova OV, Zaborsky OS. Indicators of a Cardiovascular System at 14–15 Years Old Boys at Short-term Biofeedback Training for Controlling of General Heart Rate Variability After Speed and Power Training: Experimental Controlled Study. Current Pediatrics. 2019;18(3):167-174. doi:10.15690/vsp.v18i3.2033. [Article in Russian].

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Received April 22, 2020.
Accepted May 22, 2020.
©2020 International Medical Research and Development Corporation.