ÜNİVERSİTE ÖĞRENCİLERİNİN BİREYSEL SAĞLIK ALGILARININ İNTERNET BAĞIMLILIK DÜZEYLERİNE ETKİSİ

Öz

Bu çalışmada amaç, üniversite öğrencilerinin bireysel sağlık algılarının internet bağımlılık düzeyi üzerinde etkisini ortaya koymaktır. Ayrıca öğrencilerin çeşitli demografik değişkenlere göre internet bağımlılık ve bireysel sağlık algılarına yönelik farklılıkları ortaya koymaktır. Çalışmaya Düzce üniversitesinde eğitim gören 286 öğrenci katılım sağlamıştır. Veri toplama aracı olarak internet bağımlılığı ölçeği ve bireysel sağlık algısı ölçekleri kullanılmıştır. Öğrencilerin internet bağımlılık düzeylerinin düşük, bireysel sağlık algılarının ise orta düzeyde olduğu tespit edilmiştir. Ortalama internet bağımlılık skorları erkek, örgün öğrenim ve orta gelir düzeyine sahip öğrencilerde diğer gruplara nispetem daha yüksektir. Öğrencilerin gelir durumuna, cinsiyete, yerleşim türüne ve eğitim türüne göre sağlık algı düzeyi değişmediği tespit edilmiştir. Üniversite öğrencilerinin sağlık algıları ile internet bağımlılığı arasında zayıf bir ilişki bulunmuştur. Bu bulgular, üniversite öğrencilerinin internet bağımlılığı ve sağlık algıları arasındaki ilişkiyi anlamak için önemli bir katkı sağlamaktadır. Ancak, bu ilişkinin karmaşıklığını daha iyi anlamak için daha fazla araştırmaya ihtiyaç olduğu unutulmamalıdır. Bu çalışma, internet bağımlılığına karşı etkili müdahale stratejileri geliştirme çabalarına yönelik gelecekteki çalışmalar için temel oluşturabilir.

Anahtar Kelimeler

• Dijital Bağımlılık
• Sağlık Algısı
• Üniversite Öğrencileri

Destekleyen Kurum

yok

Proje Numarası

yok

Etik Beyan

The board granted permission for ethical suitability with its decision number E-18457941-050.99-83654 dated February 28, 2023.

Teşekkür

yok

THE IMPACT OF UNIVERSITY STUDENTS' INDIVIDUAL HEALTH PERCEPTIONS ON INTERNET ADDICTION LEVELS

Öz

The aim of this study is to investigate the impact of university students' individual health perceptions on the level of internet addiction. Additionally, the study aims to reveal differences in internet addiction and individual health perceptions among students based on various demographic variables. A total of 286 students from Düzce University participated in the study. The data collection tools used were the Internet Addiction Scale and Individual Health Perception Scales. It was found that the students' levels of internet addiction were low, while their individual health perceptions were at a moderate level. The average internet addiction scores were relatively higher for male students, those enrolled in formal education, and those with a moderate income level compared to other groups. It was observed that the level of health perception did not vary according to students' income status, gender, type of settlement, and type of education. A weak relationship was identified between university students' health perceptions and internet addiction. These findings contribute significantly to understanding the relationship between internet addiction and health perceptions among university students. However, it should be noted that further research is needed to better comprehend the complexity of this relationship. This study may serve as a foundation for future research aiming to develop effective intervention strategies against internet addiction.

Anahtar Kelimeler

• Digital Addiction
• Health Perception
• University Students

Proje Numarası

yok

Kaynakça

1. Balta, O. C., & Horzum, M. B. (2008). Internet addiction test. J Educ Sci Pract, 7(13).
2. Bryman, A., & Cramer, D. (2001). Quantitative data analysis with SPSS release 10 for Windows. Routledge.
3. Chemnad, K., Aziz, M., Abdelmoneium, A., et al. (2023). Adolescents’ Internet addiction: Does it all begin with their environment? Child Adolesc Psychiatry Ment Health, 17, 87.
4. Chen, B., Liu, F., Ding, S., Ying, X., Wang, L., & Wen, Y. (2019a). Psychological capital and internet addiction among Chinese university students: The mediating role of self-esteem and resilience. Int J Environ Res Public Health, 16(24), 5057.
5. Chen, X., Li, M., Wang, P., & Bai, Y. (2019b). The relationship between online health information seeking and health-promoting behavior among Chinese university students: A mediation analysis. Front Psychol, 10, 2378.
6. Cheng, C., Li, A. Y., & Liu, J. W. (2019c). Internet addiction prevalence and quality of (real) life: A meta-analysis of 31 nations across seven world regions. Cyberpsychol Behav Soc Netw, 22(8), 540-547.
7. Diamond, J. J., Becker, J. A., Arenson, C. A., Chambers, C. V., & Rosenthal, M. P. (2007). Development of a scale to measure adults' perceptions of health: preliminary findings. J Community Psychol, 35, 557-561.
8. European Parliament. (2020). Potentially negative effects of internet use. Retrieved from https://www.europarl.europa.eu/RegData/etudes/IDAN/2020/641540/EPRS_IDA(2020)641540_EN.pdf

9. Fallah Mehneh, S., Alavi, S. S., Mirzaian, B., & Taheri Mobarakeh, S. (2020). A study of internet addiction and its effects on mental health: A study based on Iranian University Students. J Educ Health Promot, 9, 295.
10. Haddock, A., Ward, N., Yu, R., & O'Dea, N. (2022). Positive Effects of Digital Technology Use by Adolescents: A Scoping Review of the Literature. International Journal of Environmental Research and Public Health, 19(21), 14009. https://doi.org/10.3390/ijerph192114009
11. Kadıoğlu, H., & Yıldız, A. (2012). The validity and reliability of the Turkish version of the health perception scale. Turkey Clinics, 32(1), 47-53.
12. Kim, H. K., & Davis, K. E. (2009). Toward a comprehensive theory of problematic Internet use: Evaluating the role of self-esteem, anxiety, flow, and the self-rated importance of Internet activities. Computers in Human Behavior, 25, 490-500.
13. Kim, J. H., Lee, J. Y., Oh, J. Y., & Ryu, J. H. (2018). The relationship between Internet addiction and psychiatric symptoms: A comparison of college students and non-students. J Psychiatr Res, 103, 71-78.
14. Kuss, D. J., & Griffiths, M. D. (2017). Social networking sites and addiction: Ten lessons learned. Int J Environ Res Public Health, 14(3), 311.
15. Kuss, D. J., Kanjo, E., & Crook-Rumsey, M. (2018). Development and validation of the internet addiction test (IAT) version for Indonesian university students. J Educ Comput Res, 56(4), 662-673.
16. Lee, Y. K., Chang, C. T., Lin, Y., & Cheng, Z. H. (2014). The dark side of smartphone usage: Psychological traits, compulsive behavior and technostress. Comput Human Behav, 31, 373-383.
17. Li, M., Deng, Y., Ren, Y., Guo, S., & He, X. (2014). Obesity status of middle school students in Xiangtan and its relationship with Internet addiction. Obesity, 22, 482-487.
18. Lin, M. P., Ko, H. C., & Wu, J. Y. (2011). Prevalence and psychosocial risk factors associated with internet addiction in a nationally representative sample of college students in Taiwan. Cyberpsychol Behav Soc Netw, 14(12), 741-746.
19. Lin, M. P., Ko, H. C., & Wu, J. Y. W. (2014). Prevalence and psychosocial risk factors associated with internet addiction in a nationally representative sample of college students in Taiwan. Cyberpsychol Behav Soc Netw, 17(12), 660-666.
20. Pallant, J. (2005). SPSS survival manual a step by step guide to data analysis using SPSS for Windows (Version 12). Allen & Unwin.
21. Park, S. K., Kim, J. Y., & Cho, C. B. (2008). Prevalence of internet addiction and correlations with family factors among South Korean adolescents. Adolescence, 43(172), 895-909.
22. Rosen, L. D., Whaling, K., Carrier, L. M., Cheever, N. A., & Rokkum, J. (2013). The media and technology usage and attitudes scale: An empirical investigation. Comput Human Behav, 29(6), 2501-2511.
23. Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). Allyn & Bacon.
24. Tabatabaee, H. R., Rezaianzadeh, A., & Jamshidi, M. (2018). Mediators in the Relationship between Internet Addiction and Body Mass Index: A Path Model Approach Using Partial Least Square. Journal of research in health sciences, 18(3), e00423.
25. Tsitsika, A., Critselis, E., Louizou, A., et al. (2011). Determinants of Internet Addiction among Adolescents: A Case-Control Study. The Scientific World Journal, 11, 866-874.
26. TUIK. (2022). Household Information Technologies (IT) Usage Survey.
27. Wang, C. C., Lu, H. C., & Lin, Y. C. (2018). Internet addiction of Taiwanese high school adolescents: Associations with academic performance, boredom proneness, and learning attitudes. J Educ Comput Res, 55(7), 1023-1047.
28. Vlassoff C. (2007). Gender differences in determinants and consequences of health and illness. Journal of Health, Population, and Nutrition, 25(1), 47–61.
29. Yen, J. Y., Yen, C. F., Chen, C. C., Chen, S. H., & Ko, C. H. (2007). Family factors of internet addiction and substance use experience in Taiwanese adolescents. Cyberpsychology & Behavior, 10(3), 323-329.
30. Young, K. S. (2010). Internet addiction: The emergence of a new clinical disorder. In K. S. Young & C. N. de Abreu (Eds.), Internet addiction: A handbook and guide to evaluation and treatment (pp. 3-17). Wiley.
31. Yu, J. I., Choi, K. A., Lee, M. J., & Li, H. M. (2019). Factors associated with internet addiction among South Korean university students. J Med Syst, 43(11), 1-9.