Citation

Gatti AA, Keir PJMS, Noseworthy MDPD, Beauchamp MKPD, Maly MRPTPD. Eur. J. Sport Sci. 2021; ePub(ePub): ePub.

Copyright

(Copyright © 2021, Informa - Taylor and Francis Group)

DOI

10.1080/17461391.2021.1902570

PMID

unavailable

Abstract

METHODS. Forty healthy adults (17 women, 23 men; mean (SD): 28.6 (7.2) years; 24.2 (2.6) kg/m(2)) participated. Kinematic analyses were conducted for 18 three-minute bicycling bouts including all combinations of 3 horizontal and 3 vertical saddle positions, and 2 crank arm lengths. For both minimum and maximum knee flexion, predictors were identified using Least Absolute Shrinkage and Selection Operator (LASSO) regression, and final models were fit using linear regression. Secondary analyses determined if saddle height equations were sex dependent.

RESULTS. The equation to predict saddle position from minimum knee flexion angle (R(2)=0.97; root mean squared error (RMSE)=1.15 cm) was: Saddle height (cm) = 7.41 + 0.82(inseam cm) - 0.1(minimum knee flexion °) + 0.003(inseam cm)(seat tube angle °). The maximum knee flexion equation (R(2)=0.97; RMSE=1.15 cm) was: Saddle height (cm) = 41.63 + 0.78(inseam cm) - 0.25(maximum knee flexion °) + 0.002(inseam cm)(seat tube angle °). The saddle height equations were not dependent on sex.

CONCLUSIONS. These equations provide a novel, practical strategy for bicycle-fit that accounts for rider anthropometrics, bicycle geometry and user-defined kinematics.

Language: en

Keywords

Injuries; Biomechanical Phenomena; Knee; Machine Learning; Saddle