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The non-sagittal knee moment vector identifies 'at risk' individuals that the knee abduction moment alone does not.

Mark A RobinsonRaihana SharirRadin RafeeuddinJos VanrenterghemCyril J Donnelly
Published in: Sports biomechanics (2021)
Multi-planar forces and moments are known to injure the anterior cruciate ligament (ACL). In ACL injury risk studies, however, the uni-planar frontal plane external knee abduction moment is frequently studied in isolation. This study aimed to determine if the frontal plane knee moment (KM-Y) could classify all individuals crossing a risk threshold compared to those classified by a multi-planar non-sagittal knee moment vector (KM-YZ). Recreationally active females completed three sports tasks-drop vertical jumps, single-leg drop vertical jumps and planned sidesteps. Peak knee abduction moments and peak non-sagittal resultant knee moments were obtained for each task, and a risk threshold of the sample mean plus 1.6 standard deviations was used for classification. A sensitivity analysis of the threshold from 1-2 standard deviations was also conducted. KM-Y did not identify all participants who crossed the risk threshold as the non-sagittal moment identified unique individuals. This result was consistent across tasks and threshold sensitivities. Analysing the peak uni-planar knee abduction moment alone is therefore likely overly reductionist, as this study demonstrates that a KM-YZ threshold identifies 'at risk' individuals that a KM-Y threshold does not. Multi-planar moment metrics such as KM-YZ may help facilitate the development of screening protocols across multiple tasks.
Keyphrases
  • anterior cruciate ligament
  • total knee arthroplasty
  • knee osteoarthritis
  • anterior cruciate ligament reconstruction
  • working memory
  • genome wide
  • machine learning
  • deep learning
  • functional connectivity