Predicting Temporal Gait Kinematics: Anthropometric Characteristics and Global Running Pattern Matter
Aurélien Patoz1,2, Thibault Lussiana3,4, Cyrille Gindre2,3
1Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland.
Frontiers in Physiology
|January 25, 2021
Summary
Running speed alone does not accurately predict stride frequency (SF) and duty factor (DF). Individual running patterns significantly impact these metrics, necessitating personalized models for accurate estimation.
Area of Science:
- Biomechanics
- Running Mechanics
- Human Movement Analysis
Background:
- Existing equations predict stride frequency (SF) and duty factor (DF) based solely on running speed.
- However, individual running patterns (GRP) influence kinematics at a given speed, potentially affecting equation accuracy.
Purpose of the Study:
- To validate existing SF and DF prediction equations considering GRP.
- To identify key predictors of SF and DF beyond running speed, including anthropometric data and GRP.
Main Methods:
- Verified prediction equations using kinematic data from 50-m runs (n=20) on a track.
- Assessed predictors of SF and DF using treadmill kinematic data (n=54), including GRP, anthropometrics, sex, and training volume.
Main Results:
- Accounting for GRP significantly improved DF prediction (≥133% variance explained) and revealed a negative relationship between GRP and DF.
- Individual SF was best modeled by a second-order polynomial equation.
- Age and height were significant predictors of SF, while GRP was a key predictor of DF, alongside speed.
Conclusions:
- Existing equations for predicting running duty factor are invalidated by individual global running patterns.
- Personalized models incorporating global running patterns, age, and height are crucial for accurate stride frequency and duty factor estimation.
