Personalization of Sports Activities as a Condition for Improving Quality of Life in the Digitalization Era

This research explains the significance of “psychophysiological data” in sports activities. It demonstrates the potential for transitioning from traditional methods of athletic preparation to personalized methods through the application of artificial intelligence methods. The authors present various methodologies for working with data in sports, employing neural network algorithms to process individual athlete data involving a set of psychophysiological characteristics throughout the stages of preparation. According to the research results, the authors identified barriers to sports accessibility in respondents’ perceptions and the reasons for respondents’ choice of sports discipline. The methodology of this research is directed toward applied and theoretical aspects. The applied aspects encompass several elements: fostering mass sports participation, establishing digital athlete profiles, optimizing training methodologies for national sports teams and major competitions, establishing a system for nurturing sports talent, and enhancing coaches’ qualifications to promote mass sports development. The theoretical aspects include the development of a model for predictive evaluation of sporting success using neural network algorithms, assessing the importance of psychophysiological indicators for athletic performance, and devising a training management model based on individual athlete data. This model adapts flexibly to changes in the athlete’s condition to optimize performance outcomes.