Modifiable (biomechanical and neuromuscular) anterior cruciate ligament (ACL) injury risk factors have been identified in laboratory settings. These risk factors were subsequently used in ACL injury prevention measures. Due to the lack of ecological validity, the use of on-field data in the ACL injury risk screening is increasingly advocated. Though, the kinematic differences between laboratory and on-field settings have never been investigated. The aim of the present study was to investigate the lower-limb kinematics of female footballers during agility movements performed both in laboratory and football field environments. Twenty-eight healthy young female talented football (soccer) players (14.9 ± 0.9 years) participated. Lower-limb joint kinematics was collected through wearable inertial sensors (Xsens Link) in three conditions: (1) laboratory setting during unanticipated sidestep cutting at 40-50°; on the football pitch (2) football-specific exercises (F-EX) and (3) football games (F-GAME). A hierarchical two-level random effect model in Statistical Parametric Mapping was used to compare joint kinematics among the conditions. Waveform consistency was investigated through Pearson's correlation coefficient and standardized z-score vector. In-lab kinematics differed from the on-field ones, while the latter were similar in overall shape and peaks. Lower sagittal plane range of motion, greater ankle eversion, and pelvic rotation were found for on-field kinematics (p < 0.044). The largest differences were found during landing and weight acceptance. The biomechanical differences between lab and field settings suggest the application of context-related adaptations in female footballers and have implications in ACL injury prevention strategies. Highlights: Talented youth female football players showed kinematical differences between the lab condition and the on-field ones, thus adopting a context-related motor strategy. Lower sagittal plane range of motion, greater ankle eversion, and pelvic rotation were found on the field. Such differences pertain to the ACL injury mechanism and prevention strategies. Preventative training should support the adoption of non-linear motor learning to stimulate greater self-organization and adaptability. It is recommended to test football players in an ecological environment to improve subsequent primary ACL injury prevention programmes.
Modifiable (biomechanical and neuromuscular) anterior cruciate ligament (ACL) injury risk factors have been identified in laboratory settings. These risk factors were subsequently used in ACL injury prevention measures. Due to the lack of ecological validity, the use of on-field data in the ACL injury risk screening is increasingly advocated. Though, the kinematic differences between laboratory and on-field settings have never been investigated. The aim of the present study was to investigate the lower-limb kinematics of female footballers during agility movements performed both in laboratory and football field environments. Twenty-eight healthy young female talented football (soccer) players (14.9 ± 0.9 years) participated. Lower-limb joint kinematics was collected through wearable inertial sensors (Xsens Link) in three conditions: (1) laboratory setting during unanticipated sidestep cutting at 40-50°; on the football pitch (2) football-specific exercises (F-EX) and (3) football games (F-GAME). A hierarchical two-level random effect model in Statistical Parametric Mapping was used to compare joint kinematics among the conditions. Waveform consistency was investigated through Pearson's correlation coefficient and standardized z-score vector. In-lab kinematics differed from the on-field ones, while the latter were similar in overall shape and peaks. Lower sagittal plane range of motion, greater ankle eversion, and pelvic rotation were found for on-field kinematics (p < 0.044). The largest differences were found during landing and weight acceptance. The biomechanical differences between lab and field settings suggest the application of context-related adaptations in female footballers and have implications in ACL injury prevention strategies. Highlights: Talented youth female football players showed kinematical differences between the lab condition and the on-field ones, thus adopting a context-related motor strategy. Lower sagittal plane range of motion, greater ankle eversion, and pelvic rotation were found on the field. Such differences pertain to the ACL injury mechanism and prevention strategies. Preventative training should support the adoption of non-linear motor learning to stimulate greater self-organization and adaptability. It is recommended to test football players in an ecological environment to improve subsequent primary ACL injury prevention programmes.
Research of non-contact anterior cruciate ligament (ACL) inj1ury risk aims to identify modifiable risk factors that are linked to the mechanisms of injury. Information from these studies is then used in the development of injury prevention programmes. However, ACL injury risk research often leans towards methods with three limitations: 1) a poor preservation of the athlete-environment rela- tionship that limits the generalisability of results, 2) the use of a strictly biomechanical approach to injury causation that is incom- plete for the description of injury mechanisms, 3) and a reductionist analysis that neglects profound information regarding human movement. This current opinion proposes three principles from an ecological dynamics perspective that address these limitations. First, it is argued that, to improve the generalisability of findings, research requires a well-preserved athlete-environment relation- ship. Second, the merit of including behaviour and the playing situation in the model of injury causation is presented. Third, this paper advocates that research benefits from conducting non- reductionist analysis (i.e., more holistic) that provides profound information regarding human movement. Together, these princi- ples facilitate an ecological dynamics approach to injury risk research that helps to expand our understanding of injury mechan- isms and thus contributes to the development of preventative measures.
Horse riding falls under the “Sport for Life” disciplines, where a long-term equestrian development can provide a clear pathway of developmental stages to help individuals, inclusive of those with a disability, to pursue their goals in sport and physical activity, providing long-term health benefits. However, the biomechanical interaction between horse and (disabled) rider is not wholly understood, leaving challenges and opportunities for the horse riding sport. Therefore, the purpose of this KIEM project is to start an interdisciplinary collaboration between parties interested in integrating existing knowledge on horse and (disabled) rider interaction with any novel insights to be gained from analysing recently collected sensor data using the EquiMoves™ system. EquiMoves is based on the state-of-the-art inertial- and orientational-sensor system ProMove-mini from Inertia Technology B.V., a partner in this proposal. On the basis of analysing previously collected data, machine learning algorithms will be selected for implementation in existing or modified EquiMoves sensor hardware and software solutions. Target applications and follow-ups include: - Improving horse and (disabled) rider interaction for riders of all skill levels; - Objective evidence-based classification system for competitive grading of disabled riders in Para Dressage events; - Identifying biomechanical irregularities for detecting and/or preventing injuries of horses. Topic-wise, the project is connected to “Smart Technologies and Materials”, “High Tech Systems & Materials” and “Digital key technologies”. The core consortium of Saxion University of Applied Sciences, Rosmark Consultancy and Inertia Technology will receive feedback to project progress and outcomes from a panel of international experts (Utrecht University, Sport Horse Health Plan, University of Central Lancashire, Swedish University of Agricultural Sciences), combining a strong mix of expertise on horse and rider biomechanics, veterinary medicine, sensor hardware, data analysis and AI/machine learning algorithm development and implementation, all together presenting a solid collaborative base for derived RAAK-mkb, -publiek and/or -PRO follow-up projects.
