The aim of the present study was to investigate if the presence of anterior cruciate ligament (ACL) injury risk factors depicted in the laboratory would reflect at-risk patterns in football-specific field data. Twenty-four female footballers (14.9 ± 0.9 year) performed unanticipated cutting maneuvers in a laboratory setting and on the football pitch during football-specific exercises (F-EX) and games (F-GAME). Knee joint moments were collected in the laboratory and grouped using hierarchical agglomerative clustering. The clusters were used to investigate the kinematics collected on field through wearable sensors. Three clusters emerged: Cluster 1 presented the lowest knee moments; Cluster 2 presented high knee extension but low knee abduction and rotation moments; Cluster 3 presented the highest knee abduction, extension, and external rotation moments. In F-EX, greater knee abduction angles were found in Cluster 2 and 3 compared to Cluster 1 (p = 0.007). Cluster 2 showed the lowest knee and hip flexion angles (p < 0.013). Cluster 3 showed the greatest hip external rotation angles (p = 0.006). In F-GAME, Cluster 3 presented the greatest knee external rotation and lowest knee flexion angles (p = 0.003). Clinically relevant differences towards ACL injury identified in the laboratory reflected at-risk patterns only in part when cutting on the field: in the field, low-risk players exhibited similar kinematic patterns as the high-risk players. Therefore, in-lab injury risk screening may lack ecological validity.
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Background: In team handball an anterior cruciate ligament (ACL) injury often occurs during landing after a jump shot. Many intervention programs try to reduce the injury rate by instructing the athletes to land safer. Video feedback is an effective way to provide feedback although little is known about its influence on landing technique in sport-specific situations. Objective: To test the effectiveness of a video overlay feedback method on landing technique in elite handball players. Method: Sixteen elite female handball players were assigned to a Control or Video Group. Both groups performed jump shots in a pre-test, two training sessions (TR1 & TR2) and a post-test. The Video Group received video feedback of an expert model with an overlay of their own jump shots in TR1 and TR2 whilst the Control Group did not. Main outcome measures were sagittal ankle, knee and hip angles during initial contact (IC), maximum (MAX) and range of motion (ROM), in addition to the Landing Error Scoring System (LESS) score. One 2x4 repeated measures ANOVA was conducted to analyze group, time and interaction effects of all kinematic outcome measures and the LESS score. Results: The Video Group displayed significant improvement in knee and hip flexion at IC, MAX and ROM. In addition, MAX ankle flexion and their LESS score improved an average of 8.1 in the pre-test to 4.0 in the post-test. When considering performance variables, no differences between Control Group and Video Group were found in shot accuracy or vertical jump height, whilst horizontal jump distance in the Video Group became greater over time. Conclusion: Overlay visual feedback is an effective method to improve landing kinematics during a sport-specific jump shot. Further research is now warranted to determine the long-term effects and transfer to training and game situations.
Factors affecting repeated sprint ability (RSA) were evaluated in a mixed-longitudinal sample of 48 elite basketball players 14 to 19 years of age (16.1±1.7 years). Players were observed on six occasions during the 2008-2009 and 2009-2010 seasons. Three basketball-specific field tests were administered on each occasion: the Shuttle Sprint Test (SST) for RSA, the Vertical Jump (VJ) for lower body explosive strength (power), and the Interval Shuttle Run Test (ISRT) for interval endurance capacity. Height and weight were measured; body composition was estimated (percent fat, lean body mass). Multilevel modeling of RSA development curve was used with 32 players (16.0±1.7 years) who had two or more observations. The 16 players (16.1±1.8 years) measured on only one occasion were used as a control group to evaluate the appropriateness of the model. Age, lower body explosive strength, and interval endurance capacity significantly contributed to RSA (p < .05). RSA improved with age from 14-17 years (p < .05) and reached a plateau at 17-19 years. Predicted RSA did not significantly differ from measured RSA in the control group (p > .05). The results suggest a potentially important role for the training of lower body explosive strength and interval endurance capacity in the development of RSA among youth basketball players. Age-specific reference values for RSA of youth players may assist basketball coaches in setting appropriate goals for individual players.
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In societies where physical activity levels are declining, stimulating sports participation in youth is vital. While sports offer numerous benefits, injuries in youth are at an all-time high with potential long-term consequences. Particularly, women football's popularity surge has led to a rise in knee injuries, notably anterior cruciate ligament (ACL) injuries, with severe long-term effects. Urgent societal attention is warranted, supported by media coverage and calls for action by professional players. This project aims to evaluate the potential of novel artificial intelligence-based technology to enhance player monitoring for injury risk, and to integrate these monitoring pathways into regular training practice. Its success may pave the way for broader applications across different sports and injuries. Implementation of results from lab-based research into practice is hindered by the lack of skills and technology needed to perform the required measurements. There is a critical need for non-invasive systems used during regular training practice and allowing longitudinal monitoring. Markerless motion capture technology has recently been developed and has created new potential for field-based data collection in sport settings. This technology eliminates the need for marker/sensor placement on the participant and can be employed on-site, capturing movement patterns during training. Since a common AI algorithm for data processing is used, minimal technical knowledge by the operator is required. The experienced PLAYSAFE consortium will exploit this technology to monitor 300 young female football players over the course of 1 season. The successful implementation of non-invasive monitoring of football players’ movement patterns during regular practice is the primary objective of this project. In addition, the study will generate key insights into risk factors associated with ACL injury. Through this approach, PLAYSAFE aims to reduce the burden of ACL injuries in female football players.
