Machine Learning–Based Prediction of Digital Obesity and Its Relationship with Problematic Internet Use and Digital Gaming Addiction among Adolescents

Tezcan Fidan; Ayşe Gürol

Machine Learning–Based Prediction of Digital Obesity and Its Relationship with Problematic Internet Use and Digital Gaming Addiction among Adolescents

Abstract

This study aimed to determine the levels of problematic internet use, digital gaming addiction, and digital obesity among high school students, to examine their interrelationships, and to predict digital obesity using machine learning methods. Between June 2024 and June 2025, a descriptive, correlational, and predictive modelling design was employed in four public high schools in XXX. The study sample consisted of 490 students selected through a multistage sampling process. Data were collected using a personal information form, the Problematic Internet Use Scale, and the Digital Game Addiction Scale. For the prediction of digital obesity, feature selection was performed with the Boruta algorithm and seven machine learning models. Problematic internet use and digital gaming addiction were at moderate levels, both significantly higher among male and low-income students (p < 0.05). A moderate positive correlation was found between the two behavioral variables. Among machine learning models, random forest and support vector machine achieved the highest predictive accuracy for digital obesity, with weekend screen time emerging as the most important predictor. Machine learning methods demonstrated high accuracy in predicting digital obesity and revealed strong associations between digital dependency behaviors. The results emphasize the need for school- and family-based interventions to reduce digital addiction and prevent digital obesity in adolescents.

Keywords

Supporting Institution

This study was supported by XXX University's BAP Coordination Unit under project number 2025/009.

Ethical Statement

Ethical approval was obtained from the XXX University Scientific Research Ethics Committee (dated 01.10.2020, decision no. 412). Institutional permission was also obtained from the XXX Provincial Directorate of National Education. The study was conducted in accordance with the principles of the Declaration of Helsinki.

References

[1] Aslan, A. (2016). Türkiyede çocukların güvenli internet kullanımında 2010-2015 yılları arasındaki değişimleri ve uygulamaların yansımaları (Doktora Tezi). Atatürk Üniversitesi, Erzurum, Türkiye.
[2] Meltwater & We Are Social. (2024). Digital Trends Report. https://wearesocial.com/uk/blog/2024/01/digital-2024/
[3] Türkiye İstatistik Kurumu. (2021). TÜİK Hane Halkı Bilişim Teknolojileri Kullanım Araştırması. (Erişim Tarihi: 10.06.2025), Erişim Adresi: https://data.tuik.gov.tr/Bulten/Index?p=Hanehalki-Bilisim-Teknolojileri-(BT)-Kullanim-Arastirmasi-2024-53492
[4] Kuss, D. J., & Griffiths, M. D. (2012). Internet gaming addiction: A systematic review of empirical research. International journal of mental health and addiction, 10, 278-296. https://doi.org/10.1007/s11469-011-9318-5
[5] Yılmaz, E., Yel, S., & Griffiths, M. D. (2018). The impact of heavy (excessive) video gaming students on peers and teachers in the school environment: A qualitative study. Addicta: The Turkish Journal on Addiction, 5(2), 147–161. http://dx.doi.org/10.15805/addicta.2018.5.2.0035
[6] Diana, N., Satriyandari, Y., Astuti, A., & Ernawati, D. (2023). Anxiety and depression in adolescents before and during Covid-19. International Journal of Public Health Excellence (IJPHE), 2(2), 536-540. https://doi.org/10.55299/ijphe.v2i2.280
[7] Islam, M. R., Tushar, M. I., Tultul, P. S., Akter, R., Sohan, M., Anjum, R., Roy, A., Hossain, M. J., Rahman, M. A., Nahar, Z., Shahriar, M., & Bhuiyan, M. A. (2022). Problematic internet use and depressive symptoms among the school-going adolescents in Bangladesh during the COVID-19 pandemic: A cross-sectional study findings. Health science reports, 6(1), e1008. https://doi.org/10.1002/hsr2.1008
[8] Çokkeser, Ş. P., & Kızıltan, G. (2023). Adölesanlarda Dijital Bağımlılık ve Obezitenin İlişkilendirilmesi. Başkent Üniversitesi Sağlık Bilimleri Fakültesi Dergisi, 8(3), 235-244. DOI: 10.30565/busbid.83052

[9] Demir, F. B., Öteles, Ü. U., & Koçoglu, E. (2023). An investigation of the relationship between digital obesity and digital literacy levels of individuals in the context of Turkey. Educational Research and Reviews, 18(3), 35-40. DOI: 10.5897/ERR2020.4310
[10] Batıgün, A. D., & Kılıç, N. (2011). İnternet bağımlılığı ile kişilik özellikleri, sosyal destek, psikolojik belirtiler ve bazı sosyo-demografik değişkenler arasındaki ilişkiler. Türk Psikoloji Dergisi, 26(67), 1–10. https://kutuphane.dogus.edu.tr/mvt/pdf.php?lng=1&pdf=0012363
[11] Esen, B. K., & Gündoğdu, M. (2010). The relationship between internet addiction, peer pressure and perceived social support among adolescents. The International Journal of Educational Researchers, 2(1), 29-36. https://dergipark.org.tr/tr/pub/ijers
[12] Kumar, D., & Debata, I. (2018). Study to assess internet usage patterns and prevalence of internet addiction among medical and engineering students of Bengaluru city. International Journal of Community Medicine and Public Health, 5(6), 2331. DOI: https://doi.org/10.18203/2394-6040.ijcmph20182153
[13] Irmak, A. Y., & Erdoğan, S. (2015). Dijital Oyun Bağımlılığı Ölçeği Türkçe formunun geçerliliği ve güvenirliliği. Anadolu Psikiyatri Dergisi, 16(1), 10-18. https://doi.org/10.5455/apd.170337
[14] Ayas, T., & Horzum, M. B. (2013). İlköğretim öğrencilerinin internet bağımlılığı ve aile internet tutumu. Turkish Psychological Counseling and Guidance Journal, 4(39). https://doi.org/10.17066/pdrd.30435
[15] Karakuş, V., Dicle, İ., & Kılıç, A. (2024). Ortaokul öğrencilerinin dijital oyun bağımlılıklarının incelenmesi. Uluslararası Bozok Spor Bilimleri Dergisi, 5(1), 59-72. https://dergipark.org.tr/en/pub/ubosbid/issue/87608/1562570
[16] Afiani, D., Widyorini, E., & Primastuti, E. (2023). Relationship Between Escapism Behavior and Neurotism Personality on Online Game Addiction in Adolescents. Analitika: Jurnal Magister Psikologi UMA, 15(1), 60–67. https://doi.org/10.31289/analitika.v15i1.9180
[17] Perez, C., Alvarez, J. J., Carbungco, A., Due, J., Ochoa, C. M., Celis, M. L., & Lobo, J. (2024). Relationship between Online Game Addiction and Mental Well-Being of High-School students during the Covid-19 Pandemic: Implications for Learning and Development. Journal of Learning for Development, 11(2), 289–303. https://doi.org/10.56059/jl4d.v11i2.1101
[18] Rosendo-Rios, V., Trott, S., & Shukla, P. (2022). Systematic literature review online gaming addiction among children and young adults: A framework and research agenda. Addictive Behaviors, 129, 107238. DOI: 10.1016/j.addbeh.2022.107238
[19] Bayrak, T., & Cihan, B. (2021). Yeni Medyada Bağımlılık Sonucu Gelişen Dijital Obezite Olgusu. Yeni Medya Elektronik Dergisi, 5(1), 78-94. DOI: 10.17932/IAU.EJNM.25480200.2021/ejnm_v5i1007
[20] Gu, Y., Coffino, J., Boswell, R., Hall, Z., & Bragg, M. A. (2021). Associations between State-Level Obesity Rates, Engagement with Food Brands on Social Media, and Hashtag Usage. International journal of environmental research and public health, 18(23), 12785. https://doi.org/10.3390/ijerph182312785
[21] Porri, D., Morabito, L. A., Cavallaro, P., La Rosa, E., Li Pomi, A., Pepe, G., & Wasniewska, M. (2024). Time to act on childhood obesity: the use of technology. Frontiers in pediatrics, 12, 1359484. https://doi.org/10.3389/fped.2024.1359484
[22] Liu, H., Wu, YC., Chau, P.H. et al. (2024). Prediction of adolescent weight status by machine learning: a population-based study. BMC Public Health 24, 1351. https://doi.org/10.1186/s12889-024-18830-1
[23] Huang, L., Huhulea, E. N., Abraham, E., Bienenstock, R., Aifuwa, E., Hirani, R., Schulhof, A., Tiwari, R. K., & Etienne, M. (2025). The Role of Artificial Intelligence in Obesity Risk Prediction and Management: Approaches, Insights, and Recommendations. Medicina, 61(2), 358. https://doi.org/10.3390/medicina61020358
[24] Lim, H., Lee, H. & Kim, J. (2023). A prediction model for childhood obesity risk using the machine learning method: a panel study on Korean children. Sci Rep 13, 10122. https://doi.org/10.1038/s41598-023-37171-4
[25] Ceyhan, E., Ceyhan A. A. & Gürcan, A. (2007). Problemli internet kullanımı ölçeğinin geçerlik ve güvenilirlik çalışmaları. Kuram ve Uygulamada Eğitim Bilimleri Dergisi, 7, 387-416.
[26] Ceyhan, A. A., & Ceyhan, E. (2009). Ergenlerde problemli internet kullanım ölçeği (PİKÖ-E) geliştirme çalışmaları. X. Ulusal Psikolojik Danışma ve Rehberlik Kongresi sözlü bildiri, Adana.
[27] Lemmens, J. S., Valkenburg, P. M., & Peter, J. (2009). Development and validation of a game addiction scale for adolescents. Media Psychology, 12(1), 77–95. https://doi.org/10.1080/15213260802669458
[28] Kursa, M. B., & Rudnicki, W. R. (2010). Feature Selection with the Boruta Package. Journal of Statistical Software, 36(11), 1–13. DOI:10.18637/jss.v036.i11
[29] Kuhn, M. (2008). Building predictive models in R using the caret package. Journal of Statistical Software, 28(5), 1–26. DOI:10.18637/jss.v028.i05
[30] Yan, G. (2016). MLmetrics: Machine Learning Evaluation Metrics. R package version 1.1.1.
[31] Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.
[32] R Core Team (2025). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
[33] Kuss, D. J., & Griffiths, M. D. (2017). Social Networking Sites and Addiction: Ten Lessons Learned. International journal of environmental research and public health, 14(3), 311. https://doi.org/10.3390/ijerph14030311
[34] Limone, P., Ragni, B., & Toto, G. A. (2023). The epidemiology and effects of video game addiction: A systematic review and meta-analysis. Acta psychologica, 241, 104047. https://doi.org/10.1016/j.actpsy.2023.104047
[35] Abdallat, M., Al-Sanouri, M., Al-Salaymeh, S., Zoubi, M., Barakat, T., Badwan, A., Alzubi, A., & Murshidi, R. (2024). Internet Gaming Disorder and Sleep Quality among Jordanian University Students: A Cross-sectional Study. Clinical practice and epidemiology in mental health : CP & EMH, 20, e17450179310269. https://doi.org/10.2174/0117450179310269240820042452
[36] Anderson, E. L., Steen, E., & Stavropoulos, V. (2017). Internet use and problematic internet use: A systematic review of longitudinal research trends in adolescence and emergent adulthood. International Journal of Adolescence and Youth, 22(4), 430–454. https://doi.org/10.1080/02673843.2016.1227716
[37] Çelik, C. B., & Odaci, H., (2013). The relationship between problematic internet use and interpersonal cognitive distortions and life satisfaction in university students. Children And Youth Services Revıew, 35(3), 505-508.
[38] Koç, A. K. (2020). Lise ve üniversite öğrencilerinde internet oyun bağımlılığı sıklığı ve etkili faktörler [Uzmanlık tezi]. Gazi Üniversitesi, Tıp Fakültesi, Halk Sağlığı Anabilim Dalı, Ankara.
[39] Dinç, B., Gül, M., Karakaya, C., & Karataş, K. (2023). Üniversite öğrencilerinde teknoloji bağımlılığı: COVID-19 pandemisi süreci üzerinden bir inceleme. Bilecik Şeyh Edebali Üniversitesi Sağlık Bilimleri Fakültesi Dergisi, 1(2), 50–70.
[40] Baloğlu, M., Şahin, R., & Arpaci, I. (2020). A review of recent research in problematic internet use: gender and cultural differences. Current opinion in psychology, 36, 124–129. https://doi.org/10.1016/j.copsyc.2020.05.008
[41] Boz, C., & Dinç, M. (2023). Examination of game addiction studies conducted in Turkey: A systematic review study. Frontiers in psychiatry, 14, 1014621. https://doi.org/10.3389/fpsyt.2023.1014621
[42] Gan, R., Zhang, X., Li, Z., Wang, X., Zhang, Y., & Liu, Y. (2024). Application of machine learning in predicting adolescent Internet behavioral addiction. Frontiers in Psychiatry, 15, 1521051. https://doi.org/10.3389/fpsyt.2024.1521051
[43] Yáñez-Sepúlveda, R., Vásquez-Bonilla, A., Olivares, R. et al. Supervised machine learning algorithms for the classification of obesity levels using anthropometric indices derived from bioelectrical impedance analysis. Sci Rep 15, 30681 (2025). https://doi.org/10.1038/s41598-025-15264-6

Details

Primary Language

English

Subjects

Machine Vision , Digital Health

Journal Section

Research Article

Authors

Tezcan Fidan This is me
0009-0006-1727-2215
Türkiye

Ayşe Gürol ^*
0000-0002-7408-5428
Türkiye

Publication Date

May 3, 2026

Submission Date

November 6, 2025

Acceptance Date

January 27, 2026

Published in Issue

Year 2026 Volume: 6 Number: 1

IZ

https://izlik.org/JA93KK97DA

Cite

RIS / Bibtex

APA

Fidan, T., & Gürol, A. (2026). Machine Learning–Based Prediction of Digital Obesity and Its Relationship with Problematic Internet Use and Digital Gaming Addiction among Adolescents. Artificial Intelligence Theory and Applications, 6(1), 18-32. https://izlik.org/JA93KK97DA

AMA

1.Fidan T, Gürol A. Machine Learning–Based Prediction of Digital Obesity and Its Relationship with Problematic Internet Use and Digital Gaming Addiction among Adolescents. AITA. 2026;6(1):18-32. https://izlik.org/JA93KK97DA

Chicago

Fidan, Tezcan, and Ayşe Gürol. 2026. “Machine Learning–Based Prediction of Digital Obesity and Its Relationship With Problematic Internet Use and Digital Gaming Addiction Among Adolescents”. Artificial Intelligence Theory and Applications 6 (1): 18-32. https://izlik.org/JA93KK97DA.

EndNote

Fidan T, Gürol A (May 1, 2026) Machine Learning–Based Prediction of Digital Obesity and Its Relationship with Problematic Internet Use and Digital Gaming Addiction among Adolescents. Artificial Intelligence Theory and Applications 6 1 18–32.

IEEE

[1]T. Fidan and A. Gürol, “Machine Learning–Based Prediction of Digital Obesity and Its Relationship with Problematic Internet Use and Digital Gaming Addiction among Adolescents”, AITA, vol. 6, no. 1, pp. 18–32, May 2026, [Online]. Available: https://izlik.org/JA93KK97DA

ISNAD

Fidan, Tezcan - Gürol, Ayşe. “Machine Learning–Based Prediction of Digital Obesity and Its Relationship With Problematic Internet Use and Digital Gaming Addiction Among Adolescents”. Artificial Intelligence Theory and Applications 6/1 (May 1, 2026): 18-32. https://izlik.org/JA93KK97DA.

JAMA

1.Fidan T, Gürol A. Machine Learning–Based Prediction of Digital Obesity and Its Relationship with Problematic Internet Use and Digital Gaming Addiction among Adolescents. AITA. 2026;6:18–32.

MLA

Fidan, Tezcan, and Ayşe Gürol. “Machine Learning–Based Prediction of Digital Obesity and Its Relationship With Problematic Internet Use and Digital Gaming Addiction Among Adolescents”. Artificial Intelligence Theory and Applications, vol. 6, no. 1, May 2026, pp. 18-32, https://izlik.org/JA93KK97DA.

Vancouver

1.Tezcan Fidan, Ayşe Gürol. Machine Learning–Based Prediction of Digital Obesity and Its Relationship with Problematic Internet Use and Digital Gaming Addiction among Adolescents. AITA [Internet]. 2026 May 1;6(1):18-32. Available from: https://izlik.org/JA93KK97DA