Predicting maximal grip strength using hand circumference.

Area of Science:

• Kinesiology
• Anthropometry
• Biomechanical Engineering

Background:

• Maximal grip strength (MGS) is a crucial indicator of overall health and functional capacity.
• Accurate prediction of normal MGS is essential for clinical assessment and research.
• Existing models for MGS prediction often involve multiple anthropometric variables.

Purpose of the Study:

• To analyze correlations between anthropometric data and MGS.
• To develop a simple, accurate predictive model for normal MGS.
• To identify the most significant anthropometric predictor of MGS.

Main Methods:

• Bilateral MGS measurements were taken from 100 subjects using three dynamometers (Jamar, Myogrip, Martin Vigorimeter).
• Anthropometric measurements included height, weight, hand, wrist, and forearm circumference, and hand and palm length.
• Stepwise multiple linear regression analysis was employed to identify predictive variables.

Main Results:

• Hand circumference demonstrated the strongest correlation with MGS across all dynamometers and both hands.
• Hand circumference was the sole anthropometric variable consistently selected in the regression analysis.
• A predictive model based solely on hand circumference yielded significant results (adjusted r² ranging from 0.473 to 0.683).

Conclusions:

• Hand circumference alone is a reliable predictor of maximal grip strength.
• A simple regression model using hand circumference can effectively predict normal MGS in both men and women.
• This finding simplifies MGS assessment and facilitates broader application in clinical and research settings.