Rapid diagnosis of stress-related states to guide health-enhancing physical activity for university students
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Автори
Olena Andrieieva
Nataliia Byshevets
Mykhailo Dudko
Ihor Synihovets
Nataliia Korzh
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Видавець
Physical Rehabilitation and Recreational Health Technologies
Анотація
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.
Опис
Andrieieva O., Byshevets N., Dudko M., Synihovets I., Korzh N. Rapid diagnosis of stress-related states to guide health-enhancing physical activity for university students. Physical Rehabilitation and Recreational Health Technologies. - 2025. - № 10(2). - P. 87–97. doi.org/10.15391/prrht.2025-10(2).04