Cluster analysis of cutting technique—a valuable approach for assessing anterior cruciate ligament injury risk?
BackgroundDespite extensive efforts to pinpoint singular biomechanical risk factors for anterior cruciate ligament (ACL) injuries, research findings are still inconclusive. By combining multiple biomechanical variables, cluster analyses could help us identify safe and risky cutting technique strateg...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Sports and Active Living |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fspor.2025.1463272/full |
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| author | Lasse Mausehund Anri Patron Sami Äyrämö Sami Äyrämö Tron Krosshaug |
| author_facet | Lasse Mausehund Anri Patron Sami Äyrämö Sami Äyrämö Tron Krosshaug |
| author_sort | Lasse Mausehund |
| collection | DOAJ |
| description | BackgroundDespite extensive efforts to pinpoint singular biomechanical risk factors for anterior cruciate ligament (ACL) injuries, research findings are still inconclusive. By combining multiple biomechanical variables, cluster analyses could help us identify safe and risky cutting technique strategies.PurposeTo identify common movement strategies during cutting maneuvers and to assess their association with ACL injury risk.MethodsA total of 754 female elite handball and football players, including 59 with a history of ACL injury, performed a sport-specific cutting task while 3D biomechanics were recorded. Over an 8-year follow-up period, 43 of these players sustained a primary ACL injury and 13 players a secondary ACL injury. Cutting technique was described using 36 discrete kinematic variables. To identify different cutting techniques, we employed a K-means clustering algorithm on data subsets involving different numbers of kinematic variables (36, 13 and 5 variables) and different sports (handball, football, and both combined). To assess the impact of the identified cutting technique clusters on ACL injury risk, we compared the proportion of injured players between these clusters using the Fisher-Freeman-Halton Exact test and adjusted rand indices (ARI).ResultsWe identified two distinguishable cutting technique clusters in the subset involving both sports and five kinematics variables (average silhouette score, ASS = 0.35). However, these clusters were formed based on sport- or task-related differences (Fisher's p < 0.001, ARI = 0.83) rather than injury-related differences (Fisher's p = 0.417, ARI = 0.00). We also found two cutting technique clusters in the handball (ASS = 0.23) and football (ASS = 0.30) subsets with five kinematic variables. However, none of these clusters appeared to be associated with ACL injury risk (Fisher's p > 0.05, ARI = 0.00).ConclusionNo safe or risky cutting technique strategies could be discerned among female elite handball and football players. Cluster analysis of cutting technique, using a K-means algorithm, did not prove to be a valuable approach for assessing ACL injury risk in this dataset. |
| format | Article |
| id | doaj-art-8eeff920d6d94d2fa8bba94e0d5756bc |
| institution | Kabale University |
| issn | 2624-9367 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Sports and Active Living |
| spelling | doaj-art-8eeff920d6d94d2fa8bba94e0d5756bc2025-02-10T06:48:51ZengFrontiers Media S.A.Frontiers in Sports and Active Living2624-93672025-02-01710.3389/fspor.2025.14632721463272Cluster analysis of cutting technique—a valuable approach for assessing anterior cruciate ligament injury risk?Lasse Mausehund0Anri Patron1Sami Äyrämö2Sami Äyrämö3Tron Krosshaug4Oslo Sports Trauma Research Center, Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo, NorwayDepartment of Computer Science, University of Helsinki, Helsinki, FinlandFaculty of Information Technology, University of Jyväskylä, Jyväskylä, FinlandHospital Nova of Central Finland, Wellbeing Services County of Central Finland, Jyväskylä, FinlandOslo Sports Trauma Research Center, Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo, NorwayBackgroundDespite extensive efforts to pinpoint singular biomechanical risk factors for anterior cruciate ligament (ACL) injuries, research findings are still inconclusive. By combining multiple biomechanical variables, cluster analyses could help us identify safe and risky cutting technique strategies.PurposeTo identify common movement strategies during cutting maneuvers and to assess their association with ACL injury risk.MethodsA total of 754 female elite handball and football players, including 59 with a history of ACL injury, performed a sport-specific cutting task while 3D biomechanics were recorded. Over an 8-year follow-up period, 43 of these players sustained a primary ACL injury and 13 players a secondary ACL injury. Cutting technique was described using 36 discrete kinematic variables. To identify different cutting techniques, we employed a K-means clustering algorithm on data subsets involving different numbers of kinematic variables (36, 13 and 5 variables) and different sports (handball, football, and both combined). To assess the impact of the identified cutting technique clusters on ACL injury risk, we compared the proportion of injured players between these clusters using the Fisher-Freeman-Halton Exact test and adjusted rand indices (ARI).ResultsWe identified two distinguishable cutting technique clusters in the subset involving both sports and five kinematics variables (average silhouette score, ASS = 0.35). However, these clusters were formed based on sport- or task-related differences (Fisher's p < 0.001, ARI = 0.83) rather than injury-related differences (Fisher's p = 0.417, ARI = 0.00). We also found two cutting technique clusters in the handball (ASS = 0.23) and football (ASS = 0.30) subsets with five kinematic variables. However, none of these clusters appeared to be associated with ACL injury risk (Fisher's p > 0.05, ARI = 0.00).ConclusionNo safe or risky cutting technique strategies could be discerned among female elite handball and football players. Cluster analysis of cutting technique, using a K-means algorithm, did not prove to be a valuable approach for assessing ACL injury risk in this dataset.https://www.frontiersin.org/articles/10.3389/fspor.2025.1463272/fullACLbiomechanicskinematicskineticsfootballhandball |
| spellingShingle | Lasse Mausehund Anri Patron Sami Äyrämö Sami Äyrämö Tron Krosshaug Cluster analysis of cutting technique—a valuable approach for assessing anterior cruciate ligament injury risk? Frontiers in Sports and Active Living ACL biomechanics kinematics kinetics football handball |
| title | Cluster analysis of cutting technique—a valuable approach for assessing anterior cruciate ligament injury risk? |
| title_full | Cluster analysis of cutting technique—a valuable approach for assessing anterior cruciate ligament injury risk? |
| title_fullStr | Cluster analysis of cutting technique—a valuable approach for assessing anterior cruciate ligament injury risk? |
| title_full_unstemmed | Cluster analysis of cutting technique—a valuable approach for assessing anterior cruciate ligament injury risk? |
| title_short | Cluster analysis of cutting technique—a valuable approach for assessing anterior cruciate ligament injury risk? |
| title_sort | cluster analysis of cutting technique a valuable approach for assessing anterior cruciate ligament injury risk |
| topic | ACL biomechanics kinematics kinetics football handball |
| url | https://www.frontiersin.org/articles/10.3389/fspor.2025.1463272/full |
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