Email this article Risk groups of internet addiction disorder
- Authors: Sidorov A.A.1, Soldatkin V.A.1, Kibitov A.O.2,3
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Affiliations:
- Rostov State Medical University
- V.M. Bekhterev National Medical Research Center of Psychiatry and Neurology
- Pavlov First Saint Petersburg State Medical University
- Issue: Vol LVI, No 4 (2024)
- Pages: 355-367
- Section: Original study arcticles
- URL: https://journal-vniispk.ru/1027-4898/article/view/281617
- DOI: https://doi.org/10.17816/nb629858
- ID: 281617
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Abstract
BACKGROUND: Since publications on the differences of gaming addiction (GA) cohorts are scarce and primarily discuss the factors of only one type (gambling or gaming), it appears relevant to study personal, psychological, morphofunctional, genetic and other traits of patients by comparing different GA types to develop targeted prevention programs. A thorough approach to studying the specific traits of patients with the developed GA or at risk of GA contributes to the development of effective social and psychological, and psychological and educational preventive programs, and improvement of psychotherapeutic approaches to such patients.
AIM: This study develops an algorithm for differentiated assessment of the Internet addiction disorder risk.
MATERIALS AND METHODS: The study of clinical psychopathology, clinical dynamics, psychology, psychometrics, and risk factors of Internet addiction disorder included an open study of 69 gaming addiction (GA) patients, including 39 Pure GA patients and 30 Gambling GA patients. The control group (CG) consisted of 40 healthy volunteers. Basic study included clinical, psychological and psychometric techniques, and the genetic method as an additional tool. Statistics were processed using the Shapiro–Wilk test, Kolmogorov–Smirnov test, Student’s t-test, Welch’s t-test, and Mann–Whitney U-test.
RESULTS: For the first time, two key gaming addiction (GA) types (Pure GA and Gambling GA) were compared by analyzing the personality traits, morphofunctional and gender attributes of GA patients. The identified traits and attributes were considered in terms of the addiction risk (both in general and differentially) in relation to various Internet addiction types. The data analysis allowed to develop mathematical models to assess the Internet addiction risk in general and its specific types, identify risk groups and refine preventive actions.
CONCLUSION: The algorithm for differentiated assessment of the Internet addiction disorder risk was developed.
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##article.viewOnOriginalSite##About the authors
Alexey A. Sidorov
Rostov State Medical University
Author for correspondence.
Email: alexe7890@mail.ru
ORCID iD: 0000-0001-6039-4089
SPIN-code: 9655-6360
Russian Federation, Rostov-on-Don
Victor A. Soldatkin
Rostov State Medical University
Email: sva-rostov@mail.ru
ORCID iD: 0000-0002-0222-3414
SPIN-code: 8608-9020
MD, Dr. Sci. (Medicine)
Russian Federation, Rostov-on-DonAlexander O. Kibitov
V.M. Bekhterev National Medical Research Center of Psychiatry and Neurology; Pavlov First Saint Petersburg State Medical University
Email: druggen@mail.ru
ORCID iD: 0000-0002-8771-625X
SPIN-code: 3718-6729
MD, Dr. Sci. (Medicine)
Russian Federation, St. Petersburg; St. PetersburgReferences
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