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Related Experiment Videos

Lunge performance and its determinants.

John Cronin1, Peter J McNair, Robert N Marshall

  • 1Sport Performance Research Centre, School of Physiotherapy, Auckland University of Technology, Auckland, New Zealand. john.cronin@aut.ac.nz

Journal of Sports Sciences
|February 18, 2003
PubMed
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Time to peak force is the key predictor of lunge performance in athletes. Combining this with other factors like body mass and flexibility offers a more comprehensive understanding of athletic movement capabilities.

Area of Science:

  • Sports Science
  • Biomechanics
  • Human Movement Analysis

Background:

  • Lunge performance is crucial for success in sports like badminton, squash, and fencing.
  • Understanding the strength predictors of lunge performance is essential for targeted training.

Purpose of the Study:

  • To identify key strength qualities that predict lunge performance in male athletes.
  • To develop statistical models for explaining variance in lunge performance.

Main Methods:

  • Thirty-one male athletes underwent unilateral maximal squat and jump squat tests.
  • Lunge performance was measured using a linear transducer, with strength, flexibility, and anthropometric data collected.
  • Stepwise multiple regression analysis was employed to determine predictors of lunge performance.

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Main Results:

  • Time to peak force was the strongest single predictor, explaining 55% of the variance in lunge performance.
  • Three-variable models explained 76-85% of the variance, differing based on whether performance was relative to body mass or absolute.
  • Models for relative performance included time to peak force, mean power, and relative strength; absolute performance models included time to peak force, leg length, and flexibility.

Conclusions:

  • Strength diagnosis can be reliably achieved with one to two trials.
  • No single strength measure can fully explain functional lunge performance.
  • Factors such as body mass, flexibility, and leg length significantly influence statistical models predicting lunge performance.