Open AccessThe Influence of Bicycle Geometry on Time-Trial Positioning Kinematics and Markers of Performance
Author(s) -
Daniel P. Heil,
Robert Pickels
Publication year - 2019
Publication title -
international journal of physical education, fitness and sports
Language(s) - English
Resource type - Journals
eISSN - 2457-0753
pISSN - 2277-5447
DOI - 10.26524/ijpefs1912
Subject(s) - kinematics , geometry , mathematics , trunk , tilt (camera) , sagittal plane , pelvic tilt , saddle , physics , medicine , anatomy , ecology , mathematical optimization , classical mechanics , biology
Studies have previously documented how changes in cycling body kinematics are related to submaximal energetics and power output, as well as cycling performance, but few have focused specifically on how body kinematics will vary with changes in bicycle geometry. This study sought to describe kinematic changes resulting from the systematic change of several bicycle geometry variables: Trunk angle (“low” and “high” positions), seat-tube angle (76° and 80°), saddle tilt angle (0° to -10°), saddle sitting position (middle or nose), as well as two types of saddles. Methods: Well-trained cyclists were kinematically evaluated across specific combinations of geometry variables using a modified cycle ergometer at a standard relative power. Standard two dimensional sagittal-view kinematics from the left side were used to summarize a collection of kinematic variables: Trunk angle, hip angle (HA), knee angle, pelvic tilt angle, and two “composite” angles called body position and pelvic position (PP). Finally, each trial was also evaluated for frontal area (FA; m2) from stationary digital photography. Data were evaluated using repeated measures ANOVA (a=0.05) to evaluate change in kinematics between trials, as well as regression analysis to determine predictability of performance markers (HA and FA) from the collection of geometry and kinematic variables. Results: Changing trunk angle had the greatest impact on other kinematic variables, while saddle type had no influence. Regression showed that geometry variables could explain 75-85% of the variability in either HA or FA, while 78-79% of the variation in HA and 83- 84% of FA was explained by PP alone. Conclusions: The composite kinematic measure PP was generally a better predictor of both HA and FA than any combination of geometry variables. These results can serve as a starting point for understanding the interactions between bicycle geometry and body kinematics, both of which are important determinants of power generation and aerodynamic drag.