Rapid diagnosis of stress-related states to guide health-enhancing physical activity for university students

Olena Andrieieva; Nataliia Byshevets; Mykhailo Dudko; Ihor Synihovets; Nataliia Korzh

doi:10.15391/prrht.2025-10(2).04

Authors

Olena Andrieieva Department of Health and Recreational Physical Activity National University of Ukraine on Physical Education and Sport, Kyiv, Ukraine https://orcid.org/0000-0002-2893-1224
Nataliia Byshevets Department of eSports and information technologies, National University of Ukraine on Physical Education and Sport, Kyiv, Ukraine https://orcid.org/0000-0001-6118-6580
Mykhailo Dudko Department of physical education, Kyiv National Economic University named after Vadym Hetman, Kyiv, Ukraine https://orcid.org/0000-0002-3955-4743
Ihor Synihovets Department of Physical Rehabilitation, National University «Chernihiv Polytechnic», Chernihiv, Ukraine https://orcid.org/0000-0002-4456-4399
Nataliia Korzh Department of physical culture and sports management, Zaporizhzhia National Polytechnic University, Zaporizhzhia, Ukraine https://orcid.org/0000-0003-0328-200X

DOI:

https://doi.org/10.15391/prrht.2025-10(2).04

Keywords:

health-enhancing physical activity, intervention, university students, program, stress-related states

Abstract

Purpose. The purpose of the study was to build a theoretical model of rapid diagnosis of stress-related states in college students and to develop a digital tool to guide health-enhancing physical activity for university students.

Material & Methods. The study involved the use of questionnaires designed to assess psychophysiological indicators (activity, mood, sleep, appetite, performance, and well-being) with the 5-point Likert scale; stress level with V. Y. Shcherbatykh method; anxiety using the Spielberg-Hanin Inventory; and the risk of PTSD using the Mississippi Scale. The data obtained were subjected to statistical analysis, namely GLZ models.

Results. The theoretical model of rapid diagnosis of stress-associated states in higher education students during the educational process was developed, which provides a reliable foundation for monitoring stress-related states and management of appropriate measures to prevent them using physical activity. The proposed model includes interconnected stages of data mining, construction of mathematical models that link the indicators of stress, anxiety, and risk of PTSD in students and other relevant variables, analysis of their parameters and quality assessment, as well as the development of a digital solution for personalized recommendations on physical activity interventions, implementation in the educational process, and feedback. Statistically significant (p<0.05) generalized regression models were constructed that establish relationships between students’ stress-related states and multidirectional stress-related factors (gender, psychophysiological indicators, physical condition, mental state, etc.). Using these models reduces the time needed to complete questionnaires by more than 8-fold. The developed models are the basis of the interactive analytical program StressCheck Pro, which is designed to obtain up-to-date information on the stress-related states in students, to collect and analyze data necessary to assess the current state of students, to monitor the effectiveness of implemented measures, and to develop individual recommendations. When used, the obtained scores of stress, anxiety, and PTSD risk do not require additional interpretation. This assesses of stress-related states more accessible and scalable in the educational process.

Conclusions. An approach to assessing students' stress, anxiety and risk of PTSD in the educational process was developed and its practical implementation was proposed using the interactive analytical program StressCheck Pro, which automates the processes of collecting, analyzing, and interpreting data necessary for monitoring stress-related states among students as well as providing guidelines for health-enhancing physical activity.

References

Ahuja, R., & Banga, A. (2019). Mental stress detection in university students using machine learning algorithms. Procedia Computer Science, 152, 349-353. https://doi.org/10.1016/j.procs.2019.05.007

Alkhawaldeh, A., Al Omari, O., Al Aldawi, S., et al. (2023). Stress factors, stress levels, and coping mechanisms among university students. Scientific World Journal, 2023, 2026971. https://doi.org/10.1155/2023/2026971

Allan, J.F., & McKenna, J. (2019). Outdoor adventure builds resilient learners for higher education: A quantitative analysis of the active components of positive change. Sports (Basel, Switzerland), 7(5), 122. https://doi.org/10.3390/sports7050122

Andrabi, M., Mumba, M., Mathews, J., Rattan, J., & Scheiner, A. (2023). The effectiveness of a yoga program on psychological and cardiovascular outcomes of undergraduate nursing students. Holistic Nursing Practice, 37(5), E69-E74. https://doi.org/10.1097/HNP.0000000000000599

Andrieieva, O., & Hakman, A. (2018). Health status and morbidity of children 11-14 years of age during school. Journal of Physical Education and Sport, 18, 1231-1236. https://doi.org/10.7752/jpes.2018.s2183

Andrieieva, O., Blystiv, T., & Byshevets, N. (2022). Assessment of the impact of outdoor activities at leisure facilities on the physical activity of 15-year-old schoolchildren during the COVID-19 pandemic. Journal of Physical Education and Sport, 22(8), 1839-1847. https://doi.org/10.7752/jpes.2022.08231

Byshevets, N., Andrieieva, O., & Dutchak, M. (2024a). The influence of physical activity on stress-related states in higher education students. Physical Education Theory and Methodology, 24(2), 245-253. https://doi.org/10.17309/tmfv.2024.2.08

Byshevets, N., Goncharova, N., & Hakman, A. (2023). Prediction of stress-related conditions in students and their prevention through health-enhancing recreational physical activity. Journal of Physical Education and Sport, 23(4), 937-943. https://doi.org/10.7752/jpes.2023.11339

Byshevets, N., Andrieieva, O., Kashuba, V., Pasichniak, L., & Lazakovych, Y. (2024b). Application of mental fitness tools in the prevention of stress-associated conditions of female students of higher education establishments. Physical Rehabilitation and Recreational Health Technologies, 9(3), 98-112. https://doi.org/10.15391/prrht.2024-9(3).01

Drozdovska, S., Andrieieva, O., Yarmak, O., & Blagii, O. (2020). Personalization of health-promoting fitness programs for young women based on genetic factors. Journal of Physical Education and Sport, 20, 331-337. https://doi.org/10.7752/jpes.2020.s1046

Guerriero, M.A., Dipace, A., Monda, A., et al. (2025). Relationship between sedentary lifestyle, physical activity and stress in university students and their life habits: A scoping review with PRISMA checklist (PRISMA-ScR). Brain Sciences, 15(1), 78. https://doi.org/10.3390/brainsci15010078

Hakman, A., Bezverkhnia, H., & Moskalenko, N. (2020). Dynamics of the physical fitness and circumference sizes of body parts as a motivation for self-improvement and self-control in students. Journal of Physical Education and Sport, 20(1), 116-122. https://doi.org/10.7752/jpes.2020.01015

Heissel, J.A., Adam, E.K., Doleac, J.L., Figlio, D.N., & Meer, J. (2021). Testing, stress, and performance: How students respond physiologically to high-stakes testing. Education Finance and Policy, 16(2), 183-208. https://doi.org/10.1162/edfp_a_00306

Högberg, B., & Horn, D. (2022). National high-stakes testing, gender, and school stress in Europe: A difference-in-differences analysis. European Sociological Review, 38(6), 975-987. https://doi.org/10.1093/esr/jcac009

Hu, B., Yang, X., & Tuo, X. (2023). The prevalence of post-traumatic stress disorder in college students by continents and national income during the COVID-19 pandemic: A meta-analysis. Frontiers in Psychology, 14, 1129782. https://doi.org/10.3389/fpsyg.2023.1129782

Li, W., Huo, S., & Yin, F. (2024). The differences in symptom networks of depression, anxiety, and sleep in college students with different stress levels. BMC Public Health, 24, 3609. https://doi.org/10.1186/s12889-024-21161-w

Lin, A.P.C., Trappey, C.V., Luan, C.-C., Trappey, A.J.C., & Tu, K.L.K. (2021). A test platform for managing school stress using a virtual reality group chatbot counseling system. Applied Sciences, 11, 9071. https://doi.org/10.3390/app11199071

Nyagwencha-Nyamweya, E.P. (2022). The effects of poverty on students' mental well-being [Master’s thesis, Bethel University]. Spark Repository. https://spark.bethel.edu/etd/865

Orikhovska, A., Andrieieva, O., & Kashuba, V. (2020). Social integration of hearing-impaired students by means of health-enhancing and recreational activities. Teorìâ ta Metodika Fìzičnogo Vihovannâ, 20(2), 86-94. https://doi.org/10.17309/tmfv.2020.2.04

Petrachkov, O., Yarmak, O., & Shostak, R. (2023). The effect of stress factors on cognitive and management functions of cadets of higher military educational institutions. Journal of Physical Education and Sport, 23, 162-169. https://doi.org/10.7752/jpes.2023.01020

Reddy, K.J., Menon, K.R., & Thattil, A. (2018). Academic stress and its sources among university students. Biomedicine & Pharmacotherapy Journal, 11(1). http://biomedpharmajournal.org/?p=19485

Ribeiro, I.J.S., Pereira, R., Freire, I.V., et al. (2018). Stress and quality of life among university students: A systematic literature review. Health Professions Education, 4(2), 70-77. https://doi.org/10.1016/j.hpe.2017.03.002

Serikova, K.D., & Mynbayeva, A.K. (2019). Diagnostics of stress and development of students’ stress resistance by creative methods. The Journal of Psychology & Sociology, 69(2), 103-110. https://doi.org/10.26577/JPsS.2019.v69.i2.010

Silva, E., Aguiar, J., Reis, L.P., Oliveira e Sá, J., Gonçalves, J., & Carvalho, V. (2019). Information system for monitoring and assessing stress among medical students. In A. Rocha, H. Adeli, L. Reis, & S. Costanzo (Eds.), New Knowledge in Information Systems and Technologies, 331-337. Springer, Cham. https://doi.org/10.1007/978-3-030-16184-2_56

Steinacker, J.M., Mechelen, W.V., & Bloch, W. (2023). Global alliance for the promotion of physical activity: The Hamburg Declaration. BMJ Open Sport & Exercise Medicine, 9, e001626. https://doi.org/10.1136/bmjsem-2023-001626

Wang, Y., Wang, X., & Wang, X. (2023). Stressors in university life and anxiety symptoms among international students: A sequential mediation model. BMC Psychiatry, 23, 556. https://doi.org/10.1186/s12888-023-05046-7

Yarmak, O., Buhaienko, T., Zhukov, O., et al. (2019). Specificity of the relationship between the volume of physical activity and the physical condition of 18-19-year-old girls. Journal of Physical Education and Sport, 19(3), 1550-1555. https://doi.org/10.7752/jpes.2019.03224

Yin, J., Chen, Z., Zhou, K., & Yu, C. (2019). A deep learning-based chatbot for campus psychological therapy. arXiv, 1910.06707.