Cadence In Youth Long-Distance Runners Is Predicted By Leg Length and Running Speed

Authors

Jeffery A. Taylor-Haas, Cincinnati Children's Hospital Medical CenterFollow
Micah C. Garcia, Cincinnati Children's Hospital Medical CenterFollow
Mitchell J. Rauh, San Diego State UniversityFollow
Shelby Peel, University of Southern MississippiFollow
Mark V. Paterno, Cincinnati Children's Hospital Medical CenterFollow
David M. Bazett-Jones, University of ToledoFollow
Kevin R. Ford, High Point UniversityFollow
Jason T. Long, Cincinnati Children's Hospital Medical Center

Document Type

Article

Publication Date

10-1-2022

School

Kinesiology and Nutrition

Abstract

Background: Lower cadence has been previously associated with injury in long-distance runners. Variations in cadence may be related to experience, speed, and anthropometric variables. It is unknown what factors, if any, predict cadence in healthy youth long-distance runners.

Research question: Are demographic, anthropometric and/or biomechanical variables able to predict cadence in healthy youth long-distance runners.

Methods: A cohort of 138 uninjured youth long-distance runners (M = 62, F = 76; Mean ± SD; age = 13.7 ± 2.7; mass = 47.9 ± 13.6 kg; height = 157.9 ± 14.5 cm; running volume = 19.2 ± 20.6 km/wk; running experience: males = 3.5 ± 2.1 yrs, females = 3.3 ± 2.0 yrs) were recruited for the study. Multiple linear regression (MLR) models were developed for total sample and for each sex independently that only included variables that were significantly correlated to self-selected cadence. A variance inflation factor (VIF) assessed multicollinearity of variables. If VIF≥ 5, variable(s) were removed and the MLR analysis was conducted again.

Results: For all models, VIF was > 5 between speed and normalized stride length, therefore we removed normalized stride length from all models. Only leg length and speed were significantly correlated (p < .001) with cadence in the regression models for total sample (R² = 51.9 %) and females (R² = 48.2 %). The regression model for all participants was Cadence = −1.251 *Leg Length + 3.665 *Speed + 254.858. The regression model for females was Cadence = −1.190 *Leg Length + 3.705 *Speed + 249.688. For males, leg length, cadence, and running experience were significantly predictive (p < .001) of cadence in the model (R² = 54.7 %). The regression model for males was Cadence = −1.268 *Leg Length + 3.471 *Speed – 1.087 *Running Experience + 261.378.

Significance: Approximately 50 % of the variance in cadence was explained by the individual’s leg length and running speed. Shorter leg lengths and faster running speeds were associated with higher cadence. For males, fewer years of running experience was associated with a higher cadence.

Publication Title

Gait & Posture

Volume

98

First Page

266

Last Page

270

Recommended Citation

Taylor-Haas, J. A., Garcia, M. C., Rauh, M. J., Peel, S., Paterno, M. V., Bazett-Jones, D. M., Ford, K. R., Long, J. T. (2022). Cadence In Youth Long-Distance Runners Is Predicted By Leg Length and Running Speed. Gait & Posture, 98, 266-270.
Available at: https://aquila.usm.edu/fac_pubs/20344