Step frequency (SF) in running has received substantial interest from researchers, coaches, therapists, and runners. It has been widely studied in controlled settings, but no published study has measured it continuously in elite-level competition. The present study used wrist-based accelerometers in consumer-grade watches to monitor SF and SF variability of competitors in the 2016 100-km World Championship road race. Using linear mixed-model regression, SF and SF variability were assessed across the race. The average SF (steps-per-minute) of competitors ( n = 20) was 182.0 spm (range: 155.4-203.1 spm). Race fluctuations in SF were influenced only by the speed the competitors were running, with faster speeds being associated with greater SF (5.6 spm/m·s-1, P < 0.001). Independently of this speed relation, SF did not significantly change over the course of the race. SF was further linked to the runner's stature (-123.1 spm/m, P = 0.01) but not significantly related to sex, weight, age, or years of experience. The SF coefficient-of-variation was inversely associated with running speed and distance covered, with runners demonstrating decreasing variability both at faster speeds and as the race progressed. Together, these results add ecological evidence to observations of a speed dependency of SF in a highly trained, elite population of runners and suggest that in road race conditions, SF changes only with speed and not fatigue. Furthermore, it presents evidence that the variability of an elite runner's SF is linked to both speed and fatigue but not to any other characteristics of the runner. The current findings are important for runners, clinicians, and coaches as they seek to monitor or manipulate SF. NEW & NOTEWORTHY Stride frequency (SF; or the synonymous "cadence") has become a popular point of monitoring and manipulation in runners. Advances in wearable technology have enabled continuous monitoring of SF. This study is the first to examine SF and SF variability patterns throughout an entire road race in elite ultramarathon runners. This adds ecological, normative data to the field's understanding of SF and demonstrates how it relates to running speed, fatigue, and individual characteristics.
Keywords: cadence; running; stride rate; variability; wearable technology.