Ilya M. Zakharov

Background. The quality of sleep significantly impacts children’s day-to-day performance, with at least 20% reporting issues with sleepiness. Valid tools for assessing the quality of sleep are needed.

Objective. In this study, we assessed the psychometric properties of the Russian version of the Pediatric Daytime Sleepiness Scale (PDSS). The initial adaptation of the PDSS was conducted on a sample from the Arctic regions of Russia. This location may have influenced the scale's generalizability due to variations in natural daylight across different areas of the country.

Design. To rectify this, we gathered a comprehensive, geographically diverse sample from Russia. This combined dataset comprised 3772 participants between 10 to 18 years of age, from nine different regions of Russia.

Results. We confirmed the unifactorial structure of the PDSS, which showed no regional effects. The psychometric analysis indicated that one item from the 8-item PDSS could be removed, thereby improving the scale's model fit. We also observed gender and age impacts on sleep quality: boys reported fewer sleep-related issues than girls, and younger children reported fewer problems than older children.

Conclusion. This study validates the usefulness and reliability of the Russian version of the PDSS, thereby enhancing its general applicability. Furthermore, we replicated previously reported age and sex effects on the sleep quality of school-aged children.

The original experimental scheme was developed to investigate athletes’ functional states (FS) dynamics. The procedure allowed modeling various FS important for predicting the professional success of athletes: psychological and physiological stress, fatigue, and optimal FS (OFS). There were two main criteria for differentiation of the FS under study: efficiency rates and the psychological and physiological costs of the achieved efficiency level. Analysis of the FS-dependent psychophysiological changes showed significant interindividual differences on a number of parameters. Thus, no single indicator could be used as effective diagnostics for the FS criteria. A minimum number of indicators need to be recorded included cardiovascular indicators (heart rate, ECG), respiration, muscle tension (EMG), and brain activity (EEG) in the range of alpha and beta waves. The main problem can be artifacts induced by movement and muscle tension. The special procedure for artifact rejection and reduction of the artifacts was developed. It allowed recording EEG, ECG, and EOG signals simultaneously. Another problem was related to the development of the mathematical algorithm to analyze individual data and differentiate patterns of the signals recorded from the athletes. An original approach to differentiate the FS – the k-means clustering algorithm – was offered based on seven psychophysiological indicators. Results of clustering showed that the k- means algorithm for seven-component vectors allows one with confidence to differentiate state of quiet wakefulness, states of psychological and physiological stress. As the number of parameters used is attenuated from seven to four (without the EEG parameters) the accuracy of distinguishing FS is significantly reduced. To construct a complete and accurate differentiation of an athlete’s FS one should collect some statistical data on the dynamics of each FS in different time periods of the person’s life – in the process of training, after successful competition, and after losing competition.