Does the McNeill Alexander model accurately predict maximum walking speed in novice and experienced race walkers?

Background: Mathematical models propose leg length as a limiting factor in determining the maximum walking velocity. This study evaluated the effectiveness of a leg length-based model in predicting maximum walking velocity in an applied race walking situation, by comparing experienced and novice race walkers during conditions where strictly no flight time (FT) was permitted and in simulated competition conditions (i.e., FT ≤ 40 ms). Methods: Thirty-four participants (18 experienced and 16 novice race walkers) were recruited for this investigation. An Optojump Next system (8 m) was used to determine walking velocity, step frequency, step length, ground contact time, and FT during race walking over a range of velocities. Comparisons were made between novice and experienced participants in predicted maximum velocity and actual velocities achieved with no flight and velocities with FT ≤ 40 ms. The technical effectiveness of the participants was assessed using the ratio of maximum velocity to predicted velocity. Results: In novices, no significant difference was found between predicted and maximum walking speeds without FT but there was a small 5.8% gain in maximum speed when FT ≤ 40 ms. In experienced race walkers, there was a significant reduction in maximum walking speed compared with predicted maximum (p < 0.01) and a 11.7% gain in maximum walking speed with FT ≤ 40 ms. Conclusion: Leg length was a good predictor of maximal walking velocity in novice walkers but not a good predictor of maximum walking speed in well-trained walkers who appear to have optimised their walking technique to make use of non-visible flight periods of less than 40 ms. The gain in velocity above predicted maximum may be a useful index of race walking proficiency.