Impact of Mobile Phone Use on School-Level Students' Mental Health
DOI:
https://doi.org/10.62997/psi.2025a-41047Keywords:
Mobile Phone Addiction, Mental Health, School-Level studentsAbstract
This study investigates the relationship between students' mental health (MH) and mobile phone addiction (MPA), addressing growing concerns about the impact of excessive phone use on school-age children. Using a quantitative research approach, data was collected through a standardized questionnaire and analyzed using Cronbach's Alpha, Composite Reliability (CR), Average Variance Extracted (AVE), and factor loadings to ensure construct validity and reliability. Structural equation modeling (SEM) was employed to examine the impact of MPA on MH. The findings reveal that mobile phone addiction significantly explains 61.5% of the variance in mental health (R² = 0.615), with a strong positive association between MPA and mental health issues (β = 0.784, p = 0.000). This indicates that excessive mobile phone use negatively affects students' mental health, potentially leading to stress, anxiety, and poor academic performance. The study highlights the need for digital wellness initiatives and interventions to promote responsible phone use and reduce screen time. Focusing specifically on school-level teenagers contributes to understanding the psychological and academic consequences of MPA, offering valuable insights for policymakers, parents, and educational institutions. The results underscore the importance of fostering mindful phone usage to safeguard students' mental well-being. Future research should explore long-term effects and develop targeted strategies to mitigate the adverse impacts of mobile phone addiction. This study emphasizes the urgency of addressing MPA to support healthier mental and academic outcomes for students.
References
Ali, S., Shah, M., & Qasim, A. (2021). Effect of use of mobile phones on the mental health of secondary school students. International Journal of Science and Innovative Research, 2(03), 4-9. https://ijesir.org/wp-content/uploads/2021/pp0100029IJESIR.pdf
Bickel, W. K., Mellis, A. M., Snider, S. E., Athamneh, L. N., Stein, J. S., & Pope, D. A. (2018). 21st century neurobehavioral theories of decision making in addiction: Review and evaluation. Pharmacology, Biochemistry, and Behavior, 164, 4–21. https://doi.org/10.1016/j.pbb.2017.09.009
Bullard, J. L. (2023). Smartphone Addiction, Enhanced Learning Difficulties, and the Future of Education: A Phenomenological Study of Secondary Christian School Students (Doctoral dissertation, Regent University).
Canatar, F., & Bilge, Y. (2023). Attachment Styles, Sense of Identity and Interpersonal Problems as Predictors of Smartphone Addiction and Nomophobia. International Social Mentality and Researcher Thinkers Journal.
Economides, A. A., & Grousopoulou, A. (2008). Use of mobile phones by male and female Greek students. International Journal of Mobile Communications, 6(6), 729. https://doi.org/10.1504/ijmc.2008.019822
Ejaz, W., Altay, S., & Naeem, G. (2023). Smartphone use and well-being in Pakistan: Comparing the effect of self-reported and actual smartphone use. Digital Health, 9, 20552076231186075. https://doi.org/10.1177/20552076231186075
Ezeh, M. A., Ezeanya, I. D., Okonkwo, E. A., Obi, L. I., Ogbozor, P. A., & Ugwu, L. E. (2021). Self-esteem and internet addiction. ESUT Journal of Social Sciences, 6(2), 172-183. https://www.esutjss.com/index.php/ESUTJSS/article/view/68
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. JMR, Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312
Fuller, C. M., Simmering, M. J., Atinc, G., Atinc, Y., & Babin, B. J. (2016). Common methods variance detection in business research. Journal of Business Research, 69(8), 3192–3198. https://doi.org/10.1016/j.jbusres.2015.12.008
García, E. L., Lesmes, I. B., Perales, A. D., Arribas, V. M., del Puy Portillo Baquedano, M., Velasco, A. M. R., ... & Chillerón, M. Á. C. (2023). Report of the Scientific Committee of the Spanish Agency for Food Safety and Nutrition (AESAN) on sustainable dietary and physical activity recommendations for the Spanish population (Vol. 1, No. 1, p. 0005E). https://doi.org/10.2903/sp.efsa.2023.FR-0005
Gelaye, B., Tadesse, M. G., Lohsoonthorn, V., Lertmeharit, S., Pensuksan, W. C., Sanchez, S. E., Lemma, S., Berhane, Y., Vélez, J. C., Barbosa, C., Anderade, A., & Williams, M. A. (2015). Psychometric properties and factor structure of the General Health Questionnaire as a screening tool for anxiety and depressive symptoms in a multi-national study of young adults. Journal of Affective Disorders, 187, 197–202. https://doi.org/10.1016/j.jad.2015.08.045
Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121. https://doi.org/10.1108/EBR-10-2013-0128
Hansson Mild, K., Hardell, L., & Carlberg, M. (2017). Pooled analysis of two Swedish case-control studies on the use of mobile and cordless telephones and the risk of brain tumors diagnosed during 1997–2003. International Journal of Occupational Safety and Ergonomics, 13(1), 63-71. https://doi.org/10.1080/10803548.2007.11076709
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43, 115-135. https://doi.org/10.1007/s11747-014-0403-8
Igarashi, T., Motoyoshi, T., Takai, J., & Yoshida, T. (2018). No mobile, no life: Self-perception and text-message dependency among Japanese high school students. Computers in Human Behavior, 24(5), 2311-2324.
Islam, S., Malik, M. I., Hussain, S., Thursamy, R., Shujahat, M., & Sajjad, M. (2018). Motives of excessive Internet use and its impact on the academic performance of business students in Pakistan. Journal of Substance Use, 23(3), 254–261. https://doi.org/10.1080/14659891.2017.1388857
Kundi, M., & Hutter, H.-P. (2009). Mobile phone base stations-Effects on wellbeing and health. Pathophysiology, 16(2–3), 123–135. https://doi.org/10.1016/j.pathophys.2009.01.008
Leung, L. (2008). Linking psychological attributes to addiction and improper use of the mobile phone among adolescents in Hong Kong. Journal of Children and Media, 2(2), 93–113. https://doi.org/10.1080/17482790802078565
Mahadevaswamy, M. (2023). The Relationship Between Internet Addiction and Psychological Well-Being Among University Students| Research Paper Detail| Scholar9. The International Journal of Indian Psychology, 11(4), 1167-1184. https://doi.org/10.25215/1104.105
Martynenko, E. V., Sultanbayeva, G. S., Matvienko, V. V., Bazanova, A. E., Martynenko, E. V., Muratova, N. F., & Martynenko, S. E. (2024). Adaptation of problematic mobile phone usage scale (PMPUS) among students from countries of the commonwealth of independent states in Russian university. Online Journal of Communication and Media Technologies, 14(4), e202463. https://doi.org/10.30935/ojcmt/15695
Pérez de Albéniz Garrote, G., Rubio, L., Medina Gómez, B., & Buedo-Guirado, C. (2021). Smartphone abuse amongst adolescents: The role of impulsivity and sensation seeking. Frontiers in Psychology, 12, 746626. https://doi.org/10.3389/fpsyg.2021.746626
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. The Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Ringle, C. M. (2015). Partial least squares structural equation modelling (PLS-SEM) using SmartPLS 3. Computational data analysis and numerical methods VII WCDANM. Portugal. https://narti.org.uk/wp-content/uploads/sites/52/2020/06/NARTI-SMART-PLS.docx
Sahu, M., Gandhi, S., & Sharma, M. K. (2019). Mobile phone addiction among children and adolescents: A systematic review: A systematic review. Journal of Addictions Nursing, 30(4), 261–268. https://doi.org/10.1097/JAN.0000000000000309
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Treating unobserved heterogeneity in PLS-SEM: A multi-method approach. In Partial least squares path modeling: Basic concepts, methodological issues and applications (pp. 197-217). Cham: Springer International Publishing.
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial least squares structural equation modeling. In Handbook of market research (pp. 587-632). Cham: Springer International Publishing.
Schulte-Körne, G. (2016). Mental health problems in a school setting in children and adolescents. Deutsches Arzteblatt International, 113(11), 183–190. https://doi.org/10.3238/arztebl.2016.0183
Takao, M., Takahashi, S., & Kitamura, M. (2009). Addictive personality and problematic mobile phone use. Cyberpsychology & Behavior: The Impact of the Internet, Multimedia and Virtual Reality on Behavior and Society, 12(5), 501–507. https://doi.org/10.1089/cpb.2009.0022
Thomée, S. (2018). Mobile phone use and mental health. A review of the research that takes a psychological perspective on exposure. International Journal of Environmental Research and Public Health, 15(12), 2692. https://doi.org/10.3390/ijerph15122692