Prediction of Kinematic and Kinetic Performance in a Drop Vertical Jump with Individual Anthropometric Factors in Adolescent Female Athletes: Implications for Cadaveric Investigations

Nathaniel A. Bates, Gregory D. Myer, Timothy Hewett

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Anterior cruciate ligament injuries are common, expensive to repair, and often debilitate athletic careers. Robotic manipulators have evaluated knee ligament biomechanics in cadaveric specimens, but face limitations such as accounting for variation in bony geometry between specimens that may influence dynamic motion pathways. This study examined individual anthropometric measures for significant linear relationships with in vivo kinematic and kinetic performance and determined their implications for robotic studies. Anthropometrics and 3D motion during a 31 cm drop vertical jump task were collected in high school female basketball players. Anthropometric measures demonstrated differential statistical significance in linear regression models relative to kinematic variables (p-range <0.01–0.95). However, none of the anthropometric relationships accounted for clinical variance or provided substantive univariate accuracy needed for clinical prediction algorithms (r2 < 0.20). Mass and BMI demonstrated models that were significant (p < 0.05) and predictive (r2 > 0.20) relative to peak flexion moment, peak adduction moment, flexion moment range, abduction moment range, and internal rotation moment range. The current findings indicate that anthropometric measures are less associated with kinematics than with kinetics. Relative to the robotic manipulation of cadaveric limbs, the results do not support the need to normalize kinematic rotations relative to specimen dimensions.

Original languageEnglish (US)
Pages (from-to)929-936
Number of pages8
JournalAnnals of Biomedical Engineering
Volume43
Issue number4
DOIs
StatePublished - Apr 1 2015
Externally publishedYes

Fingerprint

Kinematics
Robotics
Kinetics
Ligaments
Biomechanics
Linear regression
Manipulators
Repair
Geometry

Keywords

  • Anterior cruciate ligament
  • Anthropometric variability
  • Cadaveric simulation
  • Knee kinematics
  • Robotic manipulator

ASJC Scopus subject areas

  • Biomedical Engineering

Cite this

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title = "Prediction of Kinematic and Kinetic Performance in a Drop Vertical Jump with Individual Anthropometric Factors in Adolescent Female Athletes: Implications for Cadaveric Investigations",
abstract = "Anterior cruciate ligament injuries are common, expensive to repair, and often debilitate athletic careers. Robotic manipulators have evaluated knee ligament biomechanics in cadaveric specimens, but face limitations such as accounting for variation in bony geometry between specimens that may influence dynamic motion pathways. This study examined individual anthropometric measures for significant linear relationships with in vivo kinematic and kinetic performance and determined their implications for robotic studies. Anthropometrics and 3D motion during a 31 cm drop vertical jump task were collected in high school female basketball players. Anthropometric measures demonstrated differential statistical significance in linear regression models relative to kinematic variables (p-range <0.01–0.95). However, none of the anthropometric relationships accounted for clinical variance or provided substantive univariate accuracy needed for clinical prediction algorithms (r2 < 0.20). Mass and BMI demonstrated models that were significant (p < 0.05) and predictive (r2 > 0.20) relative to peak flexion moment, peak adduction moment, flexion moment range, abduction moment range, and internal rotation moment range. The current findings indicate that anthropometric measures are less associated with kinematics than with kinetics. Relative to the robotic manipulation of cadaveric limbs, the results do not support the need to normalize kinematic rotations relative to specimen dimensions.",
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