Lumbar Spine Injury in Indian Fast Bowlers: 3D Biomechanical Analysis and Prevention Strategies

S. Arumugam,Suresh Perumal,Sai Aditya Raman,Prakash Ayyadurai, S. S. Nimishaanth, K. A. Thiagarajan

Indian Journal of Orthopaedics(2023)

引用 0|浏览2
暂无评分
摘要
Background Lumbar spine injuries are among the most common overuse injuries in a fast bowler. Among various causative factors, bowling action technique is a crucial one. Three-dimensional motion analysis has been accepted as a gold standard tool to identify incorrect techniques. Previous studies have identified key biomechanical variables associated with lumbar injury risk in fast bowlers. Despite the large popularity of the sport, there is limited information available on the subject in Indian fast bowlers. This study aims to analyse the lumbar spine injury risk in Indian fast bowlers with respect to key biomechanical variables, using 3D motion analysis. Methods Forty-seven male first class fast bowlers underwent 3D motion analysis in an indoor biomechanics laboratory. Motion capture was done with 3D cameras and 2D video cameras, using a standard marker set. Data processing and analysis was done using proprietary software. Biomechanical variables associated with lumbar spine injury risk including lateral trunk flexion (LTF) and knee angle at front foot contact (KA at FFC) were measured, and peak vertical ground reaction forces (pVGRF) were simultaneously recorded using force plates. Descriptive analysis of the data was done. Results 26% of bowlers had a high LTF, 29% had low KA at FFC and 43% had high pVGRF. Thus, a large proportion of bowlers in this study were at risk of lumbar spine injury with respect to the assessed variables. Conclusion This highlights the role of 3D motion analysis in early identification of injurious techniques, which can be modified by coaching and training interventions to prevent injuries. This study thus has implications on coaching and training of fast bowlers in India.
更多
查看译文
关键词
Cricket,Fast bowling,Lumbar spine injuries,Biomechanics
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要