Advanced Journal of Management, Humanity and Social Science

Abstract Sports injuries remain a major concern in both professional and recreational athletics, leading to significant physical, psychological, and economic consequences. Recent advances in biomechanics have provided a powerful framework for analyzing movement patterns, loading mechanisms, and tissue responses associated with sports-related injuries. This paper explores how biomechanical data analysis contributes to the understanding and prevention of sports injuries. Using motion capture systems, force plates, electromyography, and wearable sensors, researchers can quantify kinematic and kinetic variables that reveal underlying risk factors such as improper joint alignment, asymmetrical loading, or excessive repetitive forces. By integrating these biomechanical indicators with machine learning algorithms, predictive models can be developed to identify athletes at higher risk before injury occurs. The paper further reviews intervention strategies, including neuromuscular training, equipment design modifications, and individualized biomechanical feedback systems. Case studies from sports such as soccer, running, and basketball demonstrate how biomechanical insights have successfully reduced injury rates through targeted prevention programs. Ultimately, the synthesis of biomechanical data with modern computational tools represents a paradigm shift from reactive to proactive injury management. This approach not only enhances athletic performance but also promotes long-term musculoskeletal health. The findings underscore the need for interdisciplinary collaboration between biomechanists, sports scientists, medical professionals, and data analysts to create comprehensive injury-prevention ecosystems supported by empirical evidence.

Abstract Understanding the biomechanical characteristics of running gait patterns is essential for optimizing performance and minimizing injury risk among long-distance runners. This study aimed to analyze the relationship between foot strike type—classified as rearfoot, midfoot, and forefoot strike—and both injury prevalence and running efficiency. A total of 60 trained long-distance runners (30 male, 30 female) were examined using 3D motion capture and ground reaction force analysis during standardized treadmill running sessions. Kinematic and kinetic parameters, including stride length, contact time, loading rate, and vertical stiffness, were compared across foot strike patterns. Statistical analysis revealed that rearfoot strikers exhibited higher vertical impact forces and greater incidence of overuse injuries, particularly in the knee and hip regions, whereas forefoot strikers demonstrated reduced impact loading but increased calf and Achilles tendon stress. Midfoot strikers showed the most balanced biomechanical profile, with moderate impact forces and optimal running economy. The findings suggest that individualized gait assessment and training interventions tailored to foot strike patterns can enhance performance efficiency while reducing injury risk in long-distance runners.