Inter-individual differences in stride frequencies during running obtained from wearable data

Abstract

The purpose of the present study was to identify factors that underlie differences among runners in stride frequency (SF) as a function of running speed. Participants (N = 256; 85.5% males and 14.5% females; 44.1 ± 9.8 years; 181.4 ± 8.4 cm; 75.3 ± 10.6 kg; mean ± SD) shared their wearable data (Garmin Inc). Individual datasets were filtered to obtain representative relationships between stride frequency (SF) and speed per individual, representing in total 16.128 h of data. The group relationship between SF (72.82 to 94.73 strides • min −1) and running speed (V) (from 1.64 to 4.68 m • s −1) was best described with SF = 75.01 + 3.006 V. A generalised linear model with random effects was used to determine variables associated with SF. Variables and their interaction with speed were entered in a stepwise forward procedure. SF was negatively associated with leg length and body mass and an interaction of speed and age indicated that older runners use higher SF at higher speed. Furthermore, run frequency and run duration were positively related to SF. No associations were found with injury incidence, athlete experience or performance. Leg length, body mass, age, run frequency and duration were associated with SFs at given speeds. KEY POINTS • On a group level, stride frequency can be described as a linear function of speed: SF (strides • min −1) = 75.01+ 3.006•speed (m • s −1) within the range of 1.64 to 4.68 m • s −1. • On an individual level, the SF-speed relation is best described with a second order polynomial. • Leg length and body mass were positively related to stride frequency while age was negatively related to stride frequency. • Run frequency and run duration were positively related to stride frequency, while running experience, performance and injury incidence were unrelated.

Predicting Temporal Gait Kinematics: Anthropometric Characteristics and Global Running Pattern Matter

“…As for treadmill data, the high and very high correlations obtained between height and mass, height and leg length, BMI and mass, and mass and leg length (r ≥ 0.81; Table 2) are in agreement with previous observations (van Oeveren et al, 2019). Indeed, these authors also obtained correlation coefficients above 0.7 except between leg length and mass (but quite close; r = 0.66).…”

“…Of note, a random effect in the quadratic s 2 term could not be used due to the fact that three random terms applied on 20 participants require >60 observations but 60 observations were made. As for the session on the treadmill, the fixed effects included speed (continuous variable), sex (categorical variable), and variables among age, height, mass, BMI, leg length, weekly running distance, and GRP that correlated to less than 0.7 to prevent collinearity (van Oeveren et al, 2019). Correlation was determined using Pearson correlation coefficient (r), calculated between each pair of previously mentioned variables.…”

“…10, percentage of variance explained, fixed effects, and random effects [variance, intra-class correlation coefficient (ICC), and likelihood ratio test (LRT)] when assessing the effect of speed (s), age, height, body mass index (BMI), weekly running distance, global running pattern (GRP), and sex on stride frequency (SF) and duty factor (DF) obtained during the session on the treadmill using a linear mixed effects model. on an individual level might be related not only to participants' intrinsic parameters such as age (Cavagna et al, 2008b, a), sex (Chapman et al, 2012), mass (van Oeveren et al, 2019, leg length (Heglund and Taylor, 1988;Cavagna et al, 1991;Marsh et al, 2004;Srinivasan and Ruina, 2006;van Oeveren et al, 2019), and training frequency and duration (Heglund and Taylor, 1988;Cavagna et al, 1991;Marsh et al, 2004;Srinivasan and Ruina, 2006;van Oeveren et al, 2019), but also to the subconscious finetuning of running biomechanics referred to as self-optimization (Moore, 2016). Nonetheless, the large random effects observed here could also be due to the small range of tested speeds (3.33-5.00 m/s); thus, applying the same linear mixed effects model to a dataset containing a larger speed range such as the one of Gray et al (2019) would prove to be useful to validate this assumption.…”

“…Indeed, van Oeveren et al (2019) observed that higher training frequency and duration were positively related to SF, but running experience, performance, and injury incidence had no impact. Interestingly, these authors also observed that on an individual level, SF as a function of speed relationship was best described with a second-order polynomial while SF increased linearly with speed on a group level (van Oeveren et al, 2019). This result contradicts the observation of Gray et al (2019) but might be due to the smaller range of speed and heterogeneous participant characteristics involved in the study of van Oeveren et al 2019.…”

“…Nevertheless, researchers observed large inter-individual differences in the spontaneous choice of SF at a given speed (Hunter and Smith, 2007;van Oeveren et al, 2017). These differences could be due to participants' intrinsic parameters such as age (Cavagna et al, 2008a,b), sex (Chapman et al, 2012), mass (van Oeveren et al, 2019), and leg length (Heglund and Taylor, 1988;Cavagna et al, 1991;Marsh et al, 2004;Srinivasan and Ruina, 2006;van Oeveren et al, 2019), where greater SF was associated with older individuals, female, lower mass, and shorter leg length. In addition, as SF could be modified by gait retraining (Lenhart et al, 2014); experience, performance, and training could also impact SF.…”