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Non-Linear Relation Between Upper Arm Volume and Maximal Effort Force Production.

Danny M Pincivero1, Rachael R Polen2, Brittany N Byrd2

  • 11 Program in Kinesiology, University of Guelph-Humber, Toronto, ON, Canada.

Journal of Applied Biomechanics
|May 30, 2019
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This study found strong, positive relationships between maximal upper limb force and arm volume in healthy adults. Non-linear modeling revealed that limb geometry accurately predicts force production capabilities.

Keywords:
coefficientelbowregression analysisrelationship

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Area of Science:

  • Biomechanics
  • Human Physiology
  • Kinesiology

Background:

  • Understanding the relationship between anthropometry and muscular strength is crucial for performance assessment.
  • Previous research has explored limb dimensions and force, but often with linear models.
  • Accurate estimation of limb volume can inform strength predictions.

Purpose of the Study:

  • To investigate the association between maximal voluntary contraction (MVC) force and upper arm volume.
  • To determine if non-linear modeling improves the prediction of force based on limb dimensions.
  • To explore sex differences in the force-volume relationship.

Main Methods:

  • Thirty healthy young adults (15 women) participated.
  • Participants performed maximal effort isokinetic elbow flexion and extension contractions.
  • Arm volume was estimated using anthropometric measures (length, circumference, skinfolds) modeled as frustra.

Main Results:

  • Significant second-order polynomial relationships were found between maximal force and arm volume (r² = 0.63–0.86).
  • Strong positive correlations were observed for both elbow flexors and extensors.
  • Non-linear modeling provided a closer geometric representation and better prediction.

Conclusions:

  • Maximal force production is strongly related to upper arm volume in healthy young adults.
  • Non-linear regression models offer a more accurate representation of the force-volume relationship.
  • Accurate anthropometric measurements and advanced modeling enhance the understanding of biomechanical capacity.