Anterior cruciate ligament (ACL) ruptures have received large attention in recent years and represent one of the most frequent healthcare emergencies among sports injuries. ACL injury is one of the most frequent causes of loss of a sports season, and it is one of the causes of long-term disability and risk of reinjuries. ACL injuries can occur during both contact and non-contact sports, and among them, high incidences have been reported for football, rugby, ice hockey, soccer, basketball and alpine skiing.
A huge effort has been expended in supporting the adoption of prevention programs that aim to reduce the incidence of ACL injuries. However, there is no agreement on how to identify areas of weakness in athletic performance associated with risk of ACL injury and how to use this information to plan injury risk management programs. Given the scarcity of studies on the effectiveness of biomechanical analyses to act as a guide to reduce ACL injuries and/or modify its risk factors, more research is needed, and, most of all, more RCTs based on a clear mechanistic understanding of how to prevent ACL loading and rupture are required. In particular, reports are rare on the state of the art that are focused on the adoption of biomechanical tools for in-the-field use and acquiring data from athletes in environments where sports participation occurs.
The objective of this Research Topic is to provide an overview on the use of biomechanics either as a screening for ACL injuries prevention or for planning return on the field after sustaining an injury or after surgery.
Subtopics of interest include (but are not limited to):
• Evaluation of healthy and post injured subjects in gait lab facilities
• Neuromechanical function in subjects at risk of ACL injury and/or following ACL injury recovery
• Interventions to improve motor function in subjects with ACL injury (e.g., movement retraining, therapeutic exercise, biofeedback, assistive devices, surgery)
• Adoption of recent instrumentation/methods in biomechanics (e.g., wearable sensors, computational models, artificial intelligence/machine learning, biofeedback systems, finite element analysis, medical imaging, image analysis)
• In-field sports analysis of the knee mechanics
Anterior cruciate ligament (ACL) ruptures have received large attention in recent years and represent one of the most frequent healthcare emergencies among sports injuries. ACL injury is one of the most frequent causes of loss of a sports season, and it is one of the causes of long-term disability and risk of reinjuries. ACL injuries can occur during both contact and non-contact sports, and among them, high incidences have been reported for football, rugby, ice hockey, soccer, basketball and alpine skiing.
A huge effort has been expended in supporting the adoption of prevention programs that aim to reduce the incidence of ACL injuries. However, there is no agreement on how to identify areas of weakness in athletic performance associated with risk of ACL injury and how to use this information to plan injury risk management programs. Given the scarcity of studies on the effectiveness of biomechanical analyses to act as a guide to reduce ACL injuries and/or modify its risk factors, more research is needed, and, most of all, more RCTs based on a clear mechanistic understanding of how to prevent ACL loading and rupture are required. In particular, reports are rare on the state of the art that are focused on the adoption of biomechanical tools for in-the-field use and acquiring data from athletes in environments where sports participation occurs.
The objective of this Research Topic is to provide an overview on the use of biomechanics either as a screening for ACL injuries prevention or for planning return on the field after sustaining an injury or after surgery.
Subtopics of interest include (but are not limited to):
• Evaluation of healthy and post injured subjects in gait lab facilities
• Neuromechanical function in subjects at risk of ACL injury and/or following ACL injury recovery
• Interventions to improve motor function in subjects with ACL injury (e.g., movement retraining, therapeutic exercise, biofeedback, assistive devices, surgery)
• Adoption of recent instrumentation/methods in biomechanics (e.g., wearable sensors, computational models, artificial intelligence/machine learning, biofeedback systems, finite element analysis, medical imaging, image analysis)
• In-field sports analysis of the knee mechanics
