Adaptation of problematic mobile phone usage scale (PMPUS) among students from countries of the commonwealth of independent states in Russian university

Elena V. Martynenko 1 * , Gulmira S. Sultanbayeva 2, Valentin V Matvienko 1, Anna E. Bazanova 1, Evgeny V. Martynenko 1, Nozima F. Muratova 3, Stanislav E. Martynenko 1
More Detail
1 Peoples’ Friendship University of Russia named after Patrice Lumumba, Moscow, RUSSIA
2 Al-Farabi Kazakh National University, Almaty, KAZAKHSTAN
3 Uzbekistan Journalism & Mass Communications University, Tashkent, UZBEKISTAN
* Corresponding Author
Online Journal of Communication and Media Technologies, Volume 14, Issue 4, Article No: e202463. https://doi.org/10.30935/ojcmt/15695
OPEN ACCESS   236 Views   58 Downloads   Published online: 07 Dec 2024
Download Full Text (PDF)

ABSTRACT

An aim of this study was to evaluate the precision and reliability of the problematic mobile phone use scale in the context of Russia and to investigate the frequency and associated factors of problematic mobile phone use among university students. The survey included a random sample of 481 university students from Moscow, Russia. The dataset was randomly split into two groups in order to support exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). An EFA helped to build the five-component framework including social dissonance, emotional impact, cognitive impact, psychosomatic impact, and loss of control. The CFA validated this structure by obtaining favorable model fit indices. Both Cronbach’s alpha and McDonald’s (2013) omega coefficients for all subscales demonstrated a high level of dependability. The application of latent profile analysis revealed three clearly defined user profiles: high-risk users, moderate users with social concerns, and low-risk users. This study presents a reliable and valid instrument for evaluating problematic mobile phone usage in the Russian setting and provides significant insights into the complex and multifaceted nature of this phenomena. The results emphasize the need to implement focused intervention measures, especially for vulnerable populations, and add to the expanding repository of research on problematic cell phone usage in many cultural settings.
Keywords: problematic mobile phone use, scale adaptation, university students, latent profile analysis, Russian context

CITATION

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

REFERENCES

  • Agus, M., Mascia, M. L., Bonfiglio, N. S., & Penna, M. P. (2022). The Italian version of the mobile phone problematic use scale for adults (MPPUS): A validation study. Heliyon, 8(12), Article e12209. https://doi.org/10.1016/j.heliyon.2022.e12209
  • Bianchi, A., & Phillips, J. G. (2005). Psychological predictors of problem mobile phone use. Cyberpsychology & Behavior, 8(1), 39–51. https://doi.org/10.1089/cpb.2005.8.39
  • Brautsch, L. A., Lund, L., Andersen, M. M., Jennum, P. J., Folker, A. P., & Andersen, S. (2023). Digital media use and sleep in late adolescence and young adulthood: A systematic review. Sleep Medicine Reviews, 68, Article 101742. https://doi.org/10.1016/j.smrv.2022.101742
  • Canale, N., Pancani, L., Pivetta, E., Moretta, T., Marino, C., Buodo, G., Vieno, A., Dalmaso, M., & Billieux, J. (2023). Heterogeneity of smartphone impact on everyday life and its relationship with personality and psychopathology: A latent profile analysis. Comprehensive Psychiatry, 120, Article 152356. https://doi.org/10.1016/j.comppsych.2022.152356
  • Chen, H., Ma, J., Guan, J., Yin, L., Shi, Z., & Zhang, Y. (2022). The impact of psychological distress on problematic smartphone use among college students: The mediating role of metacognitions about smartphone use. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.932838
  • Cheng, Y., & Zhang, Z. (2020). A study on the influence of mobile phone addiction on academic burnout in postgraduate students with mediating effect of sleep quality. E3S Web of Conferences, 218, Article 04019. https://doi.org/10.1051/e3sconf/202021804019
  • Creswell, J. W., & Clark, V. L. P. (2017). Designing and conducting mixed methods research. SAGE.
  • Della Vedova, A. M., Covolo, L., Muscatelli, M., Loscalzo, Y., Giannini, M., & Gelatti, U. (2022). Psychological distress and problematic smartphone use: Two faces of the same coin? Findings from a survey on young Italian adults. Computers in Human Behavior, 132, Article 107243. https://doi.org/10.1016/j.chb.2022.107243
  • 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
  • Eichenberg, C., Schott, M., & Schroiff, A. (2021). Problematic smartphone use–Comparison of students with and without problematic smartphone use in light of personality. Frontiers in Psychiatry, 11. https://doi.org/10.3389/fpsyt.2020.599241
  • Foerster, M., Roser, K., Schoeni, A., & Röösli, M. (2015). Problematic mobile phone use in adolescents: Derivation of a short scale MPPUS-10. International Journal of Public Health, 60(2), 277–286. https://doi.org/10.1007/s00038-015-0660-4
  • Fook, C. Y., Narasuman, S., Aziz, N. A., Mustafa, S. M. S., & Han, C. T. (2021). Smartphone usage among university students. Asian Journal of University Education, 17(1), 283–291. https://doi.org/10.24191/ajue.v17i1.12622
  • Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Prentice Hall.
  • He, X., & Lopez, E. (2023). Level of insomnia, symptoms of depression, and anxiety among college students with mobile phone addiction: Basis for guidance and counseling program enhancement. Open Journal of Applied Sciences, 13(10), 1641–1648. https://doi.org/10.4236/ojapps.2023.1310130
  • Hu, H., Yang, X., Mo, P. K. H., Zhao, C., Kuang, B., Zhang, G., & Lin, G. (2022). How mobile phone addiction is associated with suicidal ideation in university students in China: Roles of depression and online social support. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.1001280
  • Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
  • Hu, Y. T., & Wang, Q. (2022). Self-control, parental monitoring, and adolescent problematic mobile phone use: Testing the interactive effect and its gender differences. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.846618
  • Jamovi. (2024). Jamovi–Open statistical software for the desktop and cloud. https://www.jamovi.org/
  • Jiang, W., Luo, J., Guan, H., Jiang, F., & Tang, Y. L. (2022). Problematic mobile phone use and life satisfaction among university students during the COVID-19 pandemic in Shanghai, China. Frontiers in Public Health, 9. https://doi.org/10.3389/fpubh.2021.805529
  • Kim, S. E., Kim, J. W., & Jee, Y. S. (2015). Relationship between smartphone addiction and physical activity in Chinese international students in Korea. Journal of Behavioral Addictions, 4(3), 200–205. https://doi.org/10.1556/2006.4.2015.028
  • Kline, R. B. (2023). Principles and practice of structural equation modeling. Guilford Publications.
  • Kwak, M. J., & Kim, D. J. (2023). Investigating psychological and motivational predictors of problematic smartphone use among smartphone-based social networking service (SNS) users. Addictive Behaviors Reports, 18, Article 100506. https://doi.org/10.1016/j.abrep.2023.100506
  • Li, B., Gu, Y., Yang, Y., Zhao, M., & Dong, Y. (2023a). The effect of problematic smartphone use on school engagement and disengagement among middle school students: The mediating role of academic procrastination and sleep quality. Journal of Adolescence, 95(5), 1033–1044. https://doi.org/10.1002/jad.12173
  • Li, G. R., Sun, J., Ye, J. N., Hou, X. H., & Xiang, M. Q. (2023b). Family functioning and mobile phone addiction in university students: Mediating effect of loneliness and moderating effect of capacity to be alone. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1076852
  • Lin, Y., Liu, Y., Fan, W., Tuunainen, V. K., & Deng, S. (2021). Revisiting the relationship between smartphone use and academic performance: A large-scale study. Computers in Human Behavior, 122, Article 106835. https://doi.org/10.1016/j.chb.2021.106835
  • López-Fernández, O., Honrubia-Serrano, M. L., & Freixa-Blanxart, M. (2012). Adaptación española del “mobile phone problem use scale” para población adolescente [Spanish adaptation of the “mobile phone problem use scale” for adolescent population]. Adicciones, 24(2), Article 123. https://doi.org/10.20882/adicciones.104
  • Mach, A., Demkow-Jania, M., Klimkiewicz, A., Jakubczyk, A., Abramowska, M., Kuciak, A., Serafin, P., Szczypiński, J., & Wojnar, M. (2020). Adaptation and Validation of the Polish version of the 10-item mobile phone problematic use scale. Frontiers in Psychiatry, 11. https://doi.org/10.3389/fpsyt.2020.00427
  • Mamasolievich, A. S., & Hayitmirzaevich, A. A. (2023). Strategic features of the development of the world telecommunication network. Journal of Advanced Scientific Research, 3(7).
  • Matar Boumosleh, J., & Jaalouk, D. (2017). Depression, anxiety, and smartphone addiction in university students–A cross sectional study. PLoS ONE, 12(8), Article e0182239. https://doi.org/10.1371/journal.pone.0182239
  • McDonald, R. P. (2013). Test theory: A unified treatment. Psychology Press. https://doi.org/10.4324/9781410601087
  • Mohammadi Kalhori, S., Mohammadi, M. R., Alavi, S. S., Jannatifard, F., Sepahbodi, G., Baba Reisi, M., Sajedi, S., Farshchi, M., KhodaKarami, R., & Hatami Kasvaee, V. (2015). Validation and psychometric properties of mobile phone problematic use scale (MPPUS) in university students of Tehran. Iranian Journal of Psychiatry, 10(1), 25–31.
  • Mohd Salleh Sahimi, H., Norzan, M. H., Nik Jaafar, N. R., Sharip, S., Ashraf, A., Shanmugam, K., Bistamam, N. S., Mohammad Arrif, N. E., Kumar, S., & Midin, M. (2022). Excessive smartphone use and its correlations with social anxiety and quality of life among medical students in a public university in Malaysia: A cross-sectional study. Frontiers in Psychiatry, 13. https://doi.org/10.3389/fpsyt.2022.956168
  • Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535–569. https://doi.org/10.1080/10705510701575396
  • Parent, N., Bond, T., Wu, A., & Shapka, J. (2022). Predicting patterns of problematic smartphone use among university students: A latent class analysis. Human Behavior and Emerging Technologies. https://doi.org/10.1155/2022/4287600
  • Pheeraphuttharangkoon, S. (2015). The adoption, use and diffusion of smartphones among adults over fifty in the UK [PhD thesis, University of Hertfordshire].
  • Pirwani, N., & Szabo, A. (2024). Could physical activity alleviate smartphone addiction in university students? A systematic literature review. Preventive Medicine Reports, 42, Article 102744. https://doi.org/10.1016/j.pmedr.2024.102744
  • Rozgonjuk, D., Saal, K., & Täht, K. (2018). Problematic smartphone use, deep and surface approaches to learning, and social media use in lecture. International Journal of Environmental Research and Public Health, 15(1), Article 92. https://doi.org/10.3390/ijerph15010092
  • Şar, A. H., & Işıklar, A. (2012). Problemli mobil telefon kullanım ölçeğinin Türkçeye uyarlanması [Adaptation of problem mobile phone use scale to Turkish]. International Journal of Human Sciences, 9(2), 264–275.
  • Shi, L., & Kopcha, T. J. (2022). Moderator effects of mobile users’ pedagogical role on science learning: A meta-analysis. British Journal of Educational Technology, 53(6), 1605–1625. https://doi.org/10.1111/bjet.13210
  • Sun, C., Sun, B., Lin, Y., & Zhou, H. (2022). Problematic mobile phone use increases with the fear of missing out among college students: The effects of self-control, perceived social support and future orientation. Psychology Research and Behavior Management, 15, 1–8. https://doi.org/10.2147/PRBM.S345650
  • Sunday, O. J., Adesope, O. O., & Maarhuis, P. L. (2021). The effects of smartphone addiction on learning: A meta-analysis. Computers in Human Behavior Reports, 4, Article 100114. https://doi.org/10.1016/j.chbr.2021.100114
  • Świątek, A. H., Szcześniak, M., Aleksandrowicz, B., Zaczkowska, D., Wawer, W., & Ścisłowska, M. (2023). Problematic smartphone use and social media fatigue: The mediating role of self-control. Psychology Research and Behavior Management, 16, 211–222. https://doi.org/10.2147/PRBM.S389806
  • Tang, S., Chen, R., Ma, Z., Li, X., Chen, J., & Zhao, J. (2024). Associations of problematic smartphone use with depressive symptoms and suicidal ideation in university students before and after the COVID-19 outbreak: A meta-analysis. Addictive Behaviors, 152, Article 107969. https://doi.org/10.1016/j.addbeh.2024.107969
  • Tashakkori, A., Johnson, R. B., & Teddlie, C. (2020). Foundations of mixed methods research: Integrating quantitative and qualitative approaches in the social and behavioral sciences. SAGE.
  • Thomée, S., Härenstam, A., & Hagberg, M. (2011). Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults–A prospective cohort study. BMC Public Health, 11, Article 66. https://doi.org/10.1186/1471-2458-11-66
  • Tian, J., Zhao, J. Y., Xu, J. M., Li, Q. L., Sun, T., Zhao, C. X., Gao, R., Zhu, L. Y., Guo, H. C., Yang, L. Bin, Cao, D. P., & Zhang, S. E. (2021). Mobile phone addiction and academic procrastination negatively impact academic achievement among Chinese medical students. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.758303
  • Wang, J., Li, W., Ding, L., & Chen, S. (2022). The relationship between duration of smartphone uses and anxiety in university students during the COVID-19 outbreak. International Journal of Environmental Research and Public Health, 19(11), Article 6620. https://doi.org/10.3390/ijerph19116620
  • Wang, X., Qiao, Y., & Wang, S. (2023). Parental phubbing, problematic smartphone use, and adolescents’ learning burnout: A cross-lagged panel analysis. Journal of Affective Disorders, 320, 442–449. https://doi.org/10.1016/j.jad.2022.09.163
  • Williams, B., Onsman, A., Brown, T., Andrys Onsman, P., & Ted Brown, P. (2010). Exploratory factor analysis: A five-step guide for novices. Journal of Emergency Primary Health Care, 8, 1–13. https://doi.org/10.33151/ajp.8.3.93
  • Yang, G. H., Cao, X. X., Fu, Y. Y., Wang, N. D., & Lian, S. L. (2024). Mobile phone addiction and academic burnout: The mediating role of technology conflict and the protective role of mindfulness. Frontiers in Psychiatry, 15. https://doi.org/10.3389/fpsyt.2024.1365914
  • Zhang, Y., Li, Y., Xia, M., Han, M., Yan, L., & Lian, S. (2023). The relationship between loneliness and mobile phone addiction among Chinese college students: The mediating role of anthropomorphism and moderating role of family support. PLoS ONE, 18(4), Article e0285189. https://doi.org/10.1371/journal.pone.0285189
  • Zhong, L., Huang, V., & Guo, S. (2022). Mobile phone paradox: A two-path model connecting mobile phone use and feeling of loneliness for Filipino domestic workers in Hong Kong. Mobile Media & Communication, 10(3), 448–467. https://doi.org/10.1177/20501579221077525
  • Zhou, D., Liu, J., Wang, T., Liu, J., & Li, G. (2022). Relationships among problematic smartphone use, mathematics anxiety, learning interest, and achievement: A multiple mediation model. Computers in Human Behavior, 129, Article 107171. https://doi.org/10.1016/j.chb.2021.107171