This innovative study addresses the prevalent issue of sports injuries, particularly focusing on ankle injuries, utilizing advanced analytical tools such as artificial intelligence (AI) and machine learning (ML). Employing a logistic regression model, the research achieves a remarkable accuracy of 90.0%, providing a robust predictive tool for identifying and classifying athletes with injuries. The comprehensive evaluation of performance metrics, including recall, precision, and F1-Score, emphasizes the model’s reliability. Key determinants like practicing sports with injury risk and kinesiophobia reveal significant associations, offering vital insights for early risk detection and personalized preventive strategies. The study’s contribution extends beyond predictive modeling, incorporating a predictive factors analysis that sheds light on the nuanced relationships between various predictors and the occurrence of injuries. In essence, this research not only advances our understanding of sports injuries but also presents a potent tool with practical implications for injury prevention in athletes, bridging the gap between data-driven insights and actionable strategies.

This study investigates the multifaceted phenomenon of kinesiophobia, characterized by an irrational fear of movement, particularly prevalent among individuals with chronic pain. The research delves into various factors influencing kinesiophobia, including demographic variables, psychological experiences, and physical characteristics. Employing the Tampa Scale of Kinesiophobia, the study assesses fear levels and their correlation with pain perception and functional capacity. Findings reveal a significant relationship between kinesiophobia and psychological exhaustion, reduced alertness, and self-esteem issues, particularly among athletes. Moreover, factors such as fatigue, overextension, sleep quality, and nutritional habits are identified as contributing to the manifestation of kinesiophobia. Furthermore, our study employs meticulous data preprocessing and neural network modeling, achieving an accuracy of 94.54%, to explore predictive factors such as age, gender, and fear-related attitudes. Notably, while injury history emerges as a significant predictor of kinesiophobia, gender does not exert a substantial influence. Notably, while injury history emerges as a significant predictor of kinesiophobia, gender does not exert a substantial influence. The analysis underscores the complex interplay of physical, psychological, and environmental factors in shaping kinesiophobia and highlights the importance of tailored interventions targeting fear-related attitudes and promoting well-being and rehabilitation strategies in individuals affected by this condition.

Research competencies are considered essential in fields such as science, academia, and technology, and this research seeks to provide a reliable tool to evaluate them. Therefore, the main objective of this study is to validate the “Cursa-T” scale through an exploratory and confirmatory factor analysis, as well as through structural equations, to ensure that the data collected fit the proposed theoretical model. The study sample consists of 1104 university students, mostly female, and a questionnaire based on previous studies is used. The most important results of the research include the validation of the “Cursa-T” scale through advanced statistical methods, such as exploratory and confirmatory factor analysis. The scale is found to be reliable and valid for measuring undergraduate research and digital competencies, and the data collected fit the proposed theoretical model. The discussion of the research highlights the importance of technology, devices, software, and the use of platforms in the development of research and digital competencies in Health Sciences students. It also reflects on the role of social networks in these competencies, as they can facilitate participation in academic communities. Ultimately, the research underlines the relevance of preparing undergraduate students in health areas.