The Association of University Students' Internet Use with Psychiatric Scales and Gender-Based Differences

Öz

In recent years, the rapidly increasing prevalence of problematic internet use has become a significant issue targeting adolescents and young adults. Given the potential for this problem to coexist with other psychiatric disorders or to serve as an indicator of underlying psychiatric conditions, a comprehensive evaluation is crucial. A cohort of 60 participants, comprising 30 female and 30 male students, was examined. The scores from the Beck Depression Inventory, Beck Anxiety Inventory, Barratt Impulsiveness Scale, Wender Utah Rating Scale, Liebowitz Social Anxiety Scale, Toronto Alexithymia Scale, and Difficulties in Emotion Regulation Scale were analyzed in conjunction with the Young Internet Addiction Test scores and daily internet usage time using various statistical methodologies. The results indicated a strong positive correlation between the Internet addiction scale and Internet usage duration with depression, anxiety, and attention deficit scores. Furthermore, female students exhibited higher levels of anxiety, depression, and social phobia scores compared to male students. In addition to statistical analyses, important machine learning scores have been achieved. Statistical analyses and machine learning outcomes demonstrated that internet addiction is associated with psychiatric disorders.

Anahtar Kelimeler

• Problematic İnternet Use
• University Youth
• Psychiatric Scales
• Gender Effect

Proje Numarası

FBA-2023-13090

Üniversiteli Gençlerin İnternet Kullanımının Psikiyatrik Ölçütlerle İlişkilendirilmesi ve Cinsiyete Bağlı Farklılıklar

Öz

Son yıllarda hızla artan problemli internet kullanımı ergenleri ve genç yetişkinleri hedef alan önemli bir sorun haline gelmiştir. Bu sorunun diğer psikiyatrik bozukluklara eşlik etme veya altta yatan psikiyatrik durumların göstergesi olma potansiyeli göz önüne alındığında, kapsamlı bir değerlendirme yapılması büyük önem taşımaktadır. Çalışmaya 30 kadın ve 30 erkek genç üniversite öğrencisi dahil edilmiştir. Beck Depresyon Envanteri, Beck Anksiyete Envanteri, Barratt Dürtüsellik Ölçeği, Wender Utah Derecelendirme Ölçeği, Liebowitz Sosyal Anksiyete Ölçeği, Toronto Aleksitimi Ölçeği ve Duygu Düzenleme Güçlüğü Ölçeği'nden alınan puanlar, çeşitli analitik yöntemler kullanılarak Young İnternet Bağımlılığı Ölçeği puanları ve günlük internet kullanım süresiyle ilişkili olarak analiz edilmiştir. İnternet bağımlılığı ölçeği ve internet kullanım süresi ile depresyon, anksiyete ve dikkat eksikliği puanları arasında güçlü sbir pozitif korelasyon olduğu görülmüştür. Ayrıca, kadınlarda erkeklere kıyasla daha yüksek düzeyde anksiyete, depresyon ve sosyal fobi skoru elde edilmiştir. İstatistiksel analizlere ek olarak, makine öğrenimi sonuçları da elde edilmiştir. İstatistiksel analizlere ve makine öğrenimi sonuçları ile internet bağımlılığının psikiyatrik bozukluklarla ilişkili olduğunu göstermiştir.

Anahtar Kelimeler

• Problemli İnternet Kullanımı
• Üniversiteli Gençler
• Psikiyatrik Ölçekler
• Cinsiyet Etkisi

Destekleyen Kurum

Erciyes Üniversitesi Bilimsel Araştırma Projeleri Birimi

Proje Numarası

FBA-2023-13090

Etik Beyan

Bu çalışma Erciyes Üniversitesi Klinik Araştırmalar Etik Kurulu tarafından 2023/340 sayılı karar ile onaylanmıştır.

Teşekkür

Bu çalışma Erciyes Üniversitesi Bilimsel Araştırma Projeleri Birimi tarafından desteklenmektedir. (Proje Kodu: FBA-2023-13090)

Kaynakça

1. C.-H. Ko, J.-Y. Yen, C.-F. Yen, C.-S. Chen, and C.-C. Chen, “The association between Internet addiction and psychiatric disorder: A review of the literature,” European Psychiatry, vol. 27, no. 1, pp. 1–8, Jan. 2012, doi: 10.1016/j.eurpsy.2010.04.011.
2. P. Peng and H. Zou, “Longitudinal relationship between internet addiction and psychotic-like experiences among Chinese college students,” Comprehensive Psychiatry, vol. 137, p. 152572, Feb. 2025, doi: 10.1016/j.comppsych.2024.152572.
3. K. S. Young, “Cognitive Behavior Therapy with Internet Addicts: Treatment Outcomes and Implications,” CyberPsychology & Behavior, vol. 10, no. 5, pp. 671–679, Oct. 2007, doi: 10.1089/cpb.2007.9971.
4. C.-H. Ko, J.-Y. Yen, C.-C. Chen, S.-H. Chen, and C.-F. Yen, “Gender Differences and Related Factors Affecting Online Gaming Addiction Among Taiwanese Adolescents,” The Journal of Nervous and Mental Disease, vol. 193, no. 4, p. 273, Apr. 2005, doi: 10.1097/01.nmd.0000158373.85150.57.
5. F.-C. Chang, C.-H. Chiu, C.-M. Lee, P.-H. Chen, and N.-F. Miao, “Predictors of the initiation and persistence of Internet addiction among adolescents in Taiwan,” Addictive Behaviors, vol. 39, no. 10, pp. 1434–1440, Oct. 2014, doi: 10.1016/j.addbeh.2014.05.010.
6. A. Weinstein and M. Lejoyeux, “Internet Addiction or Excessive Internet Use,” The American Journal of Drug and Alcohol Abuse, vol. 36, no. 5, pp. 277–283, Aug. 2010, doi: 10.3109/00952990.2010.491880.
7. N. Kim, T. L. Hughes, C. G. Park, L. Quinn, and I. D. Kong, “Resting-State Peripheral Catecholamine and Anxiety Levels in Korean Male Adolescents with Internet Game Addiction,” Cyberpsychology, Behavior, and Social Networking, vol. 19, no. 3, pp. 202–208, Mar. 2016, doi: 10.1089/cyber.2015.0411.
8. X. Xie, H. Cheng, and Z. Chen, “Anxiety predicts internet addiction, which predicts depression among male college students: A cross-lagged comparison by sex,” Front. Psychol., vol. 13, Jan. 2023, doi: 10.3389/fpsyg.2022.1102066.

9. O. O. Demirtaş, A. Alnak, and M. Coşkun, “Lifetime depressive and current social anxiety are associated with problematic internet use in adolescents with ADHD: a cross-sectional study,” Child and Adolescent Mental Health, vol. 26, no. 3, pp. 220–227, 2021, doi: 10.1111/camh.12440.
10. A. Mahapatra and P. Sharma, “Association of Internet addiction and alexithymia – A scoping review,” Addictive Behaviors, vol. 81, pp. 175–182, Jun. 2018, doi: 10.1016/j.addbeh.2018.02.004.
11. S. Baysan-Arslan, S. Cebeci, M. Kaya, and M. Canbal, “Relationship between internet addiction and alexithymia among university students,” Clinical and Investigative Medicine, vol. 39, no. 6, pp. S111–S115, Dec. 2016, doi: 10.25011/cim.v39i6.27513.
12. A. Germani et al., “The Relationships between Compulsive Internet Use, Alexithymia, and Dissociation: Gender Differences among Italian Adolescents,” International Journal of Environmental Research and Public Health, vol. 20, no. 14, Art. no. 14, Jan. 2023, doi: 10.3390/ijerph20146431.
13. M. Boysan, D. J. Kuss, Y. Barut, N. Ayköse, M. Güleç, and O. Özdemir, “Psychometric properties of the Turkish version of the Internet Addiction Test (IAT),” Addictive Behaviors, vol. 64, pp. 247–252, Jan. 2017, doi: 10.1016/j.addbeh.2015.09.002.
14. L. Widyanto and M. McMurran, “The Psychometric Properties of the Internet Addiction Test,” CyberPsychology & Behavior, vol. 7, no. 4, pp. 443–450, Aug. 2004, doi: 10.1089/cpb.2004.7.443.
15. M. Lyvers, J. Karantonis, M. S. Edwards, and F. A. Thorberg, “Traits associated with internet addiction in young adults: Potential risk factors,” Addictive Behaviors Reports, vol. 3, pp. 56–60, Jun. 2016, doi: 10.1016/j.abrep.2016.04.001.
16. M. Ulusoy, N. H. Sahin, and H. Erkmen, “Turkish version of the Beck Anxiety Inventory: psychometric properties,” Journal of cognitive psychotherapy, vol. 12, no. 2, p. 163, 1998.
17. E. Uğurgöl et al., “Doğrusal olmayan EEG dinamikleri ile anksiyete tespiti,” Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, vol. 13, no. 2, pp. 1–1, 2024.
18. M. S. Stanford, C. W. Mathias, D. M. Dougherty, S. L. Lake, N. E. Anderson, and J. H. Patton, “Fifty years of the Barratt Impulsiveness Scale: An update and review,” Personality and Individual Differences, vol. 47, no. 5, pp. 385–395, Oct. 2009, doi: 10.1016/j.paid.2009.04.008.
19. M. F. Ward, “The Wender Utah Rating Scale: an aid in the retrospective diagnosis of childhood attention deficit hyperactivity disorder,” American journal of Psychiatry, vol. 150, pp. 885–885, 1993.
20. R. G. Heimberg et al., “Psychometric properties of the Liebowitz Social Anxiety Scale,” Psychological Medicine, vol. 29, no. 1, pp. 199–212, Jan. 1999, doi: 10.1017/S0033291798007879.
21. G. Jackson-Koku, “Beck Depression Inventory,” Occupational Medicine, vol. 66, no. 2, pp. 174–175, Mar. 2016, doi: 10.1093/occmed/kqv087.
22. E. G. Kapci, R. Uslu, H. Turkcapar, and A. Karaoglan, “Beck Depression Inventory II: evaluation of the psychometric properties and cut-off points in a Turkish adult population,” Depression and Anxiety, vol. 25, no. 10, pp. E104–E110, 2008, doi: 10.1002/da.20371.
23. R. M. Bagby, J. D. A. Parker, and G. J. Taylor, “Twenty-five years with the 20-item Toronto Alexithymia Scale,” Journal of Psychosomatic Research, vol. 131, p. 109940, Apr. 2020, doi: 10.1016/j.jpsychores.2020.109940.
24. S. Obeid et al., “Factors associated with alexithymia among the Lebanese population: results of a cross-sectional study,” BMC Psychol, vol. 7, no. 1, p. 80, Dec. 2019, doi: 10.1186/s40359-019-0353-5.
25. D. Demirpence Secinti and E. Sen, “Reliability and validity of the brief version of the difficulties in emotion regulation scale in a sample of Turkish adolescents,” BMC Psychol, vol. 11, no. 1, p. 165, May 2023, doi: 10.1186/s40359-023-01199-y.
26. S. Bagheri, S. Taridashti, H. Farahani, P. Watson, and E. Rezvani, “Multilayer perceptron modeling for social dysfunction prediction based on general health factors in an Iranian women sample,” Front. Psychiatry, vol. 14, Dec. 2023, doi: 10.3389/fpsyt.2023.1283095.
27. F. T. Cruz, E. E. C. Flores, and S. J. C. Quispe, “Prediction of depression status in college students using a Naive Bayes classifier based machine learning model,” Jul. 25, 2023, arXiv: arXiv:2307.14371. doi: 10.48550/arXiv.2307.14371.
28. J. L. Fleiss, J. B. W. Williams, and A. F. Dubro, “The logistic regression analysis of psychiatric data,” Journal of Psychiatric Research, vol. 20, no. 3, pp. 195–209, Jan. 1986, doi: 10.1016/0022-3956(86)90003-8.
29. G. Orrù, W. Pettersson-Yeo, A. F. Marquand, G. Sartori, and A. Mechelli, “Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review,” Neuroscience & Biobehavioral Reviews, vol. 36, no. 4, pp. 1140–1152, Apr. 2012, doi: 10.1016/j.neubiorev.2012.01.004.
30. Ž. Vujović, “Classification model evaluation metrics,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 6, pp. 599–606, 2021.
31. A. Nagaur, “Internet Addiction and Mental Health among University students during CVOID-19 lockdown,” Mukt Shabd Journal, vol. 9, no. 5, pp. 684–692, 2020.
32. T. Gao et al., “Trajectories of depression and anxiety in Chinese high school freshmen: Associations with Internet addiction,” Journal of Affective Disorders, vol. 286, pp. 180–186, May 2021, doi: 10.1016/j.jad.2021.02.074.
33. R. Pandey, P. Saxena, and A. Dubey, “Emotion regulation difficulties in alexithymia and mental health,” Europe’s Journal of Psychology, vol. 7, no. 4, Art. no. 4, Nov. 2011, doi: 10.5964/ejop.v7i4.155.
34. C.-H. Ko, J.-Y. Yen, C.-S. Chen, C.-C. Chen, and C.-F. Yen, “Psychiatric Comorbidity of Internet Addiction in College Students: An Interview Study,” CNS Spectrums, vol. 13, no. 2, pp. 147–153, Feb. 2008, doi: 10.1017/S1092852900016308.
35. F. Bahrami and N. Yousefi, “Females Are More Anxious Than Males: a Metacognitive Perspective,” Iran J Psychiatry Behav Sci, vol. 5, no. 2, pp. 83–90, 2011.
36. Y. Gan et al., “Application of machine learning in predicting adolescent Internet behavioral addiction,” Front. Psychiatry, vol. 15, Apr. 2025, doi: 10.3389/fpsyt.2024.1521051.
37. O. V. Klochko, V. M. Fedorets, and V. I. Klochko, “Empirical comparison of clustering and classification methods for detecting Internet addiction,” CTE Workshop Proceedings, vol. 11, pp. 273–302, Mar. 2024, doi: 10.55056/cte.664.
38. S. S. Deniz, “İnternet Bağımlılığı Skorlarının Tahmininde Farklı Makine Öğrenme Modellerinin Karşılaştırılması,” JISS, no. 50, Art. no. 50, Dec. 2024, doi: 10.61904/sbe.1567234.