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Depresyonun Akıllı Telefon Bağımlılığı Üzerindeki Yordayıcı Rolünün İncelenmesi

Year 2024, , 156 - 165, 27.06.2024
https://doi.org/10.35365/ctjpp.24.2.06

Abstract

Bu araştırmanın amacı depresyonun akıllı telefon bağımlılığı üzerindeki yordayıcı rolünü incelemektir. Çalışmaya 219’u kadın, 107’si erkek olmak üzere toplam 326 yetişkin birey katılmıştır. Katılımcıların yaşları 18 ile 61 yaş arasında değişmekte olup, yaş ortalaması 26.86±8.05’tir. Araştırma kapsamında kişisel bilgi formu, DASS-21 (Depresyon, Anksiyete ve Stres Ölçeği) depresyon alt ölçeği ve Akıllı Telefon Bağımlılığı Ölçeği-Kısa Formu uygulanmıştır. Kişisel bilgi formunda yaş ve cinsiyet ile ilgili sorular yer almaktadır. DASS-21 depresyon alt ölçeğinin bu araştırmadaki iç tutarlık katsayısı .91 olarak bulunmuştur. Akıllı Telefon Ölçeği-Kısa Formunun bu araştırmadaki Cronbach alfa katsayısı .88 olarak belirlenmiştir. Pearson Korelasyon analizinden elde edilen bulgular depresyonun akıllı telefon bağımlılığı ile negatif yönde ilişkili (r=.391, p<.001) olduğunu göstermiştir. Depresyonun akıllı telefon bağımlılığı üzerindeki yordayıcı rolünü incelemek için yapısal eşitlik modeli kullanılmıştır. Yapısal eşitlik modelinden elde edilen sonuçlara göre depresyonun akıllı telefon bağımlılığını pozitif yönde ve anlamlı olarak (β = 0.416, p < .001) yordadığı tespit edilmiştir. Depresyonun akıllı telefon bağımlılığında meydana gelen değişimin % 17’sini açıkladığı bulunmuştur. Bulgular ilgili literatür bağlamında tartışılmış ve önleyici ruh sağlığı kapsamında araştırmacılara öneriler sunulmuştur.

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References

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Examining the Predictive Role of Depression on Smartphone Addiction

Year 2024, , 156 - 165, 27.06.2024
https://doi.org/10.35365/ctjpp.24.2.06

Abstract

The aim of this study is to examine the predictive role of depression on smartphone addiction. A total of 326 adults, 219 women and 107 men, participated in the study. The ages of the participants ranged between 18 and 61 years old, and the average age was 26.86±8.05. Within the scope of the research, a personal information form, DASS-21 (Depression, Anxiety and Stress Scale) depression subscale and Smartphone Addiction Scale-Short Form were applied. The personal information form includes questions about age and gender. The internal consistency coefficient of the DASS-21 depression subscale in this study was found to be .91. The Cronbach’s alpha coefficient of the Smartphone Scale-Short Form in this study was determined as .88. Findings from Pearson Correlation analysis showed that depression as negatively associated with smartphone addiction (r=.391, p<.001). Structural equation modeling was used to examine the predictive role of depression on smartphone addiction. According to the results obtained from the structural equation model, depression was found to predict smartphone addiction positively and significantly (β = 0.416, p < .001). Depression was found to explain 17% of the variation in smartphone addiction. The findings were discussed in the context of the relevant literature and suggestions were made to researchers within the scope of preventive mental health.

Project Number

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References

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  • Ataş, H., & Çelik, B. (2019). Smartphone use of university students: Patterns, purposes, and situations. Malaysian Online Journal of Educational Technology, 7(2), 54-70. https://doi.org/10.17220/mojet.2019.02.004
  • Baker, T.B., Piper, M. E., McCarthy, D.E., Majeskie, M. R., & Fiore, M. C. (2004). Addiction motivation reformulated: An affective processing model of negative reinforcement. Psychological Review, 111(1), 33-51. https://doi.org/10.1037/0033-295X.111.1.33
  • Bian, M., & Leung, L. (2015). Linking loneliness, shyness, smartphone addiction symptoms, and patterns of smartphone use to social capital. Social Science Computer Review, 33(1), 61–79. https://doi.org/10.1177/0894439314528779
  • Billieux, J., Maurage, P., Lopez-Fernandez, P., Kuss, D. J., & Griffiths, M. D. (2015). Can disordered mobile phone use be considered a behavioral addiction? An update on current evidence and a comprehensive model for future research. Current Addiction Reports, 2(2), 156–162. https:// doi.org/10.1007/s40429-015-0054-y
  • Boumosleh, J. M., & Jaalouk, D. (2017). Depression, anxiety, and smartphone addiction in university students- A cross-sectional study. PLoS One, 12(8), e0182239. https//doi.org/10.1371/journal.pone.0182239
  • Busch, P. A., & McCarthy, S. (2021). Antecedents and consequences of problematic smartphone use: A systematic literature review of an emerging research area. Computers in Human Behavior, 114, Article 106414. https://doi.org/10.1016/j.chb.2020.106414
  • Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications programming (2nd. ed.). Routledge.Cheng, Y., & Meng, J. (2021). The association between depression and problematic smartphone behaviors through smartphone use in a clinical sample. Human Behavior and Emerging Technologies, 3(3), 441-453. https://doi.org/10.1002/hbe2.258
  • Demirci, K., Akgönül, M., & Akpinar, A. (2015). Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. Journal of Behavioral Addictions, 4(2), 85-92. https://doi.org/10.1556/2006.4.2015.010
  • Deng, T., Kanthawala, S., Meng, J., Peng, W., Kononova, A., Hao, Q., Zhang, Q., & David, P. (2018). Measuring smartphone usage and task switching with log tracking and self-reports. Mobile Media & Communication, 7(1), 3–23. https://doi.org/10.1177/2050157918761491
  • Derevensky, J., Hayman, V., & Gilbeau, L. (2019). Behavioral addictions: Excessive gambling, gaming, internet, and smartphone use among children and adolescents. Pediatric Clinics of North America, 66(6), 1163-1182. https://doi.org/10.1016/j.pcl.2019.08.008
  • Ding, Y., Wan, X., Lu, G., Huang, H., Liang, Y., Yu, J., & Chen, C. (2022). The associations between smartphone addiction and self-esteem, self-control, and social support among Chinese adolescents: A meta-analysis. Frontiers in Psychology, 13 (1029323). https://doi.org/10.3389/fpsyg.2022.1029323
  • Elhai, J. D., Levine, J. C., Dvorak, R. D., & Hall, B. J. (2017). Non-suicidal features of smartphone use are most related to depression, anxiety and problematic smartphone use. Computers in Human Behavior, 69, 75-82. https://doi.org/10.1016/j.chb.2016.12.023
  • Elhai, J. D., Levine, J. C., & Hall, B. J. (2019). The relationship between anxiety symptom severity and problematic smartphone use: A review of the literature and conceptual frameworks. Journal of Anxiety Disorders, 62, 45–52. https://doi.org/10.1016/j.janxdis.2018.11.005
  • Elhai, J. D., Yang, H., Fang, J., Bai, X., & Hall, B. J. (2020). Depression and anxiety symptoms are related to problematic smartphone use severity in Chinese young adults: Fear of missing out as a mediator. Addictive Behaviors, 101, 105962. https://doi.org/10.1016/j.addbeh.2019. 04.020
  • Faul, F., Erdfelder, E., Lang, A., G., & Bucher, A. (2007). G*Power 3: a flexible statistical power analysis program for the social behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175-191. https://doi.org/10.3758/bf03193146
  • Field, A. (2013). Discovering Statistics Using IBM SPSS (4th ed.). Sage Publications. Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2015). How to design and evaluate research in education (9th ed.). McGraw Hill Education.
  • Frommer, D. (2011, June 6). History less: How the iPhone changed smartphones forever. Business Insider.
  • Gao, Y., Li, A., Zhu, T., Liu, X., & Liu, X. (2016). How smartphone usage correlates with social anxiety and loneliness. Peer J 4: e2197. https://doi.org/10.7717/peerj.2197
  • Geng, Y., Gu, J., Wang, J., & Zhang, R. (2021). Smartphone addiction and depression, anxiety: The role of bedtime procrastination and self-control. Journal of Affective Disorders, 293, 415-421. https://doi.org/10.1016/j.jad.2021.06.062
  • George, D., & Mallery, P. (2019). IBM SPSS statistics 25 step by step: A Simple Guide and Reference (15th ed.). Routledge.
  • Green, M. C., Brock, T. C., & Kaufman, G. F. (2004). Understanding media enjoyment: The role of transportation into narrative worlds. Communication Theory, 14(4), 311–327. https://doi.org/10.1111/j.1468-2885.2004.tb00317.x
  • Hoyle, R. H. (2014). Handbook of structural equation modeling. Guilford Press.
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There are 64 citations in total.

Details

Primary Language English
Subjects Developmental Psychology (Other)
Journal Section Research Articles
Authors

Mehmet Enes Sağar 0000-0003-0941-5301

Feridun Kaya 0000-0001-9549-6691

Yüksel Eroğlu 0000-0002-0028-0327

Durmuş Sinan 0000-0002-5980-3145

Yılmaz Çelik 0009-0005-8902-8716

Cafer Tosun 0009-0009-1472-971X

Project Number -
Publication Date June 27, 2024
Submission Date March 30, 2024
Acceptance Date May 19, 2024
Published in Issue Year 2024

Cite

APA Sağar, M. E., Kaya, F., Eroğlu, Y., Sinan, D., et al. (2024). Examining the Predictive Role of Depression on Smartphone Addiction. Kıbrıs Türk Psikiyatri Ve Psikoloji Dergisi, 6(2), 156-165. https://doi.org/10.35365/ctjpp.24.2.06