Practical Insights into Crank Arm Length Selection: Effects on Cycling Performance and Metrics in an Augmented Reality Environment

Boram Lim; Christopher M. Hearon

doi:10.28985/1526.jsc.04

Authors

Boram Lim Department of Kinesiology, Texas A&M University, Corpus Christi, TX
Christopher M. Hearon Department of Health and Kinesiology, Texas A&M University, Kingsville, TX

DOI:

https://doi.org/10.28985/1526.jsc.04%20

Keywords:

endurance cycling, virtual cycling environment, smallest worthwhile change

Abstract

The purpose of this study was to identify whether cycling time trial (TT) performance and cycling metrics are affected by small changes in crank arm length (CAL). Fourteen subjects (8 male; 6 female) completed three cycling TTs with different CALs (i.e., 165, 170, and 175 mm) on three separate occasions along a designated virtual course (distance: 11.65 km, elevation: 34.1 m) using a smart bike integrated with augmented reality (AR) software. A repeated measures ANOVA was conducted to analyze the effect of CAL on various cycling metrics: cadence (rpm), pace (m×s^-1), power (W×kg^-1), TT performance (min), energy expenditure (kJ), heart rate (bpm), and rate of perceived exertion (RPE), with appropriate post hoc tests as needed. Cadence, pace, and TT performance showed significant main effects across CALs (p < 0.05). However, no statistical differences were observed in cycling power, energy expenditure, average heart rate, or RPE (p > 0.05). Smallest worthwhile change (SWC) analysis revealed that performance differences between 165 and 170 mm may still hold practical significance despite statistically non-significant post hoc comparisons (p > 0.05). These findings suggest that a shorter CAL is associated with faster completion time under controlled conditions due to higher cadence and faster pace without increasing physiological strain. Practically, recreational cyclists and novice triathletes may consider slightly shorter crank arms to improve short-to-moderate distance cycling performance. In addition, an augmented reality platform combined with a smart bike provides a reproducible and ecologically valid method for training and applied performance assessment.

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