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

Abstract

Students are considered a vulnerable population category to stress-related disorders (Byshevets et al., 2023; 2024; Orikhovska, et al., 2021; Petrachkov et al., 2023). According to the latest data, 48.8% of the surveyed people demonstrate medium and high levels of stress (Reddy et al, 2018). Moreover, increased stress levels exacerbate symptoms of depression, anxiety, and sleep disorders (Li et al., 2024). The authors emphasize that stress has a negative effect on the health of students and reduces their quality of life and academic success (Hakman et al., 2020; Ribeiro et al., 2018). Among the factors that cause mental health disorders of the studied population, scientists name educational workload, difficulties in mastering educational material, sessions, exams, work on projects, and unfavorable demographic indicators (Heissel et al., 2021; Högberg et al., 2022; Nyagwencha-Nyamweya, 2022; Wang et al., 2023). At the same time, the COVID-19 outbreak and subsequent quarantine restrictions have caused an increase in cases of stress-related conditions among college students (Andrieieva et al., 2023). The authors point to the negative impact of stressful events, such as natural disasters and anthropogenic trauma, on mental health and emphasize that such events can lead to PTSD and depression (Hu et al., 2025). In particular, the prevalence of post-traumatic stress disorder (PTSD) among students during COVID-19 ranged from 20.0% (North America) to 48.95% (Africa) depending on the continent and reached an average of 25% (Hu et al., 2025). For Ukrainian students, the situation is even more threatening. Due to the military operations in the country, they are subjected to significant psychological pressure, feeling constant anxiety for the future and fear for the life and health of themselves and their relatives. As a result, according to our previous research, 53.7% of male students and 64.0% of female students are characterized by a medium or high risk of developing PTSD (Byshevets et al., 2023; 2024). Studies have provided convincing evidence of the effectiveness of preventing stress-related conditions in students using physical activity (PA) (Andrieieva & Hakman, 2018). However, for their effective use and continuous monitoring of stressrelated conditions in higher education students, there is a need to develop a tool that will allow for a quick and accurate assessment of individual levels of stress, anxiety, and the risk of PTSD in the educational process. Analysis of scientific information indicates a growing interest among scientists in issues related to the spread of stressrelated conditions in higher education students (Guerriero et al., 2025). As a diagnostic method, scientists choose such psychological stress scales as PSM25 (LemurTesierFillion), the questionnaire “Typical behavior in stressful situations” (CBS), the technique “Comprehensive assessment of stress manifestations” (Serikova & Mynbayeva, 2019) they propose to calculate stress using heart rate, EMG, GSR data of the hands and feet, breathing, using ECG signals (electrocardiogram) (Ahuja & Banga, 2015). Digital tools have been developed to automate the diagnosis. J. Yin reported on the development and implementation of a virtual platform to detect signs of depression, anxiety, stress, and mood in college students living on campus (Yin et al., 2019). A. Lin proposed a digital platform, the VR Group Counseling (VRGC) system, through which students can receive psychological help and stress management counseling through interaction with a chatbot or during communication with a real counselor (Lin et al., 2021). E. Silva proposed the EUSTRESS information system aimed at systematic monitoring of stress-related states of medical students in real time (Silva et al., 2019). The calculations are based on a neural model developed by machine learning. Despite the existing developments, a tool that will provide reliable data collection for rapid diagnosis of stress-related states among university students and the development of personalized health and recreation programs requires scientific substantiation and development (Drozdovska et al., 2020; Steinacker et al., 2023; Yarmak et al., 2019). Integrating the tool into a digital educational platform will ensure convenient access and prompt processing of results. The purpose of the study was to build a theoretical model for rapid diagnosis of stress-related states in students and to develop a digital tool for its implementation in the educational process.