A predictive model of the psychophysiological state of students with an analysis of anxiety and cardiorespiratory coordination: A pilot study
DOI:
https://doi.org/10.33910/2687-0223-2025-7-3-216-226Keywords:
students, clinoorthostatic test, cardiorespiratory coordination, anxiety, psychophysiological stateAbstract
High school students and first-year university students are highly susceptible to psycho-emotional stress. Compared to the general population, their anxiety levels are significantly elevated, especially during the pre-exam period. Prolonged stress can lead to dysregulation of cardiac and respiratory rhythms and an increased risk of developing cardiovascular and other diseases. This underscores the importance of developing programs to prevent morbidity and maintain the health of school and university students. One promising approach is the creation of predictive models for assessing the psychophysiological state of individuals.
This study aims to evaluate the possibility of creating a predictive model of physiological and psychometric indicators of students at the end of a semester based on psychophysiological data obtained at the beginning of the semester.
A longitudinal study was conducted involving 150 students aged 19–20 years. A modified clinoorthostatic test with ECG and respiratory rate recording was performed twice: in September and December. Before the clinoorthostatic test, participants completed the Spielberger–Khanin situational and personal anxiety test. Principal components analysis was performed for each student based on the time series of R-peak frequency of the ECG and respiratory movements in two states (lying and standing). The PLS regression method was used to analyze the relationships between the data matrices.
A model was created to predict the psychophysiological state of undergraduates at the end of a semester based on cardio-respiratory and anxiety measures at the beginning of the semester. The values of the indicators predicted by the PLS regression prior to the exams in December showed a significant correlation with the measured values, demonstrating the possibility of predicting assessment results before the exams based on the screening data collected in September.
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