Edited by: Olivier Girard, Murdoch University, Australia
Reviewed by: John Mercer, University of Nevada, Las Vegas, United States; Giorgos Paradisis, National and Kapodistrian University of Athens, Greece
This article was submitted to Elite Sports and Performance Enhancement, a section of the journal Frontiers in Sports and Active Living
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Trained endurance runners appear to fine-tune running mechanics to minimize metabolic cost. Referred to as self-optimization, the support for this concept has primarily been collated from only a few gait (e.g., stride frequency, length) and physiological (e.g., oxygen consumption, heart rate) characteristics. To extend our understanding, the aim of this study was to examine the effect of manipulating ground contact time on the metabolic cost of running in trained endurance runners. Additionally, the relationships between metabolic cost, and leg stiffness and perceived effort were examined. Ten participants completed 5 × 6-min treadmill running conditions. Self-selected ground contact time and step frequency were determined during habitual running, which was followed by ground contact times being increased or decreased in four subsequent conditions whilst maintaining step frequency (2.67 ± 0.15 Hz). The same self-selected running velocity was used across all conditions for each participant (12.7 ± 1.6 km · h−1). Oxygen consumption was used to compute the metabolic cost of running and ratings of perceived exertion (RPE) were recorded for each run. Ground contact time and step frequency were used to estimate leg stiffness. Identifiable minimums and a curvilinear relationship between ground contact time and metabolic cost was found for all runners (
Self-optimization is the subconscious, fine-tuning of running mechanics to minimize metabolic cost (Cavanagh and Williams,
Reported associations between ground contact time and metabolic cost have been equivocal (Moore,
Morin et al. (
Assessing a runner's ground contact time can be performed with relatively simple equipment, such as a video camera or phone application (Balsalobre-Fernández et al.,
The primary aim of this study was to examine the effect of manipulating ground contact time on the metabolic cost of running, in addition to determining the effect of altered leg stiffness on the metabolic cost of running. Based on the self-optimization theory, it was hypothesized that the trained runners would self-select a ground contact time and leg stiffness near to their mathematically derived metabolically optimal ground contact time (within 5%). The mathematical optimal being an identifiable minimum in a curvilinear relationship between metabolic cost and the gait characteristics. A secondary aim was to assess the relationship between metabolic cost and perceived exertion across the different ground contact time conditions and we hypothesized that a positive, linear relationship would be observed between metabolic cost and perceived exertion.
Ten trained, university level endurance runners (nine male and one female) provided informed, written consent to participate in the study (age: 19.8 ± 2.6 years; height: 1.79 ± 0.12 m; mass: 65.1 ± 6.6 kg). Each participant completed a minimum of two structured training sessions per week, were part of the athletics first team squad and could run sub-17 min 5 km or sub-35 min (male) and sub-45 min 10 km (female). Additionally, all participants were familiar with treadmill running and had been injury-free for the previous 6 months. Ethical approval was obtained from the University's Ethics Committee.
All running conditions were undertaken during one visit to the laboratory. Mass and height were measured prior to commencing the warm-up. All participants performed a self-selected warm-up for between 5 and 10 min during which time they were familiarized to the cues that were about to be provided. Participants were then instructed to self-select a running velocity that they believed they could comfortably maintain for 30 min. Participants then completed 5 × 6-min treadmill runs at their self-selected running velocity (12.7 ± 1.6 km·h−1), with 3 min rest periods between consecutive bouts. The self-selected running velocity was deemed to be submaximal based on data from the habitual condition producing a respiratory exchange ratio <1.0 during the final 2 min of the run. During the first run, participants performed their habitual running technique, which allowed their self-selected step frequency and ground contact time to be determined via the
Participants were video recorded in the frontal plane using the
Oxygen consumption data were filtered using a recursive low-pass, second order Butterworth filter (0.43 Hz cut-off frequency determined using residual analysis) and the mean metabolic cost was computed using the final 2 min of each run. Datasets were checked for outliers (2 SD away from the mean) and any within-participant outliers for each run were removed prior to the mean metabolic cost being calculated. All oxygen consumption data were visually checked for the presence of a steady-state. Metabolic cost was computed using the oxygen consumed per unit body mass per unit time (ml O2 · kg−1 · min−1). Using metabolic cost per unit distance (ml O2 · kg−1 · km−1) rather than per unit time did not alter the relationships identified in the study.
Optimal ground contact time was determined separately for each participant using the metabolic cost. Specifically, a least-squares cubic interpolation (third order polynomial, interpolated to fifty data points) with ground contact time as the independent variable and metabolic cost as the dependent variable was calculated. Cubic interpolation was employed to accommodate the potential asymmetrical increase in metabolic cost either side of the optimum and any asymmetrical increases and decreases in ground contact, as the magnitudes of ground contact time changes could not be controlled. The cubic interpolation was constrained by the habitual ground contact time and oxygen consumption being a known, fixed point on the third order polynomial. The minimum of the cubic interpolation was identified using the
Example measured (filled circles) and interpolated (dotted line) data showing the relationship between the deviations from the self-selected running gait characteristics (%) and from the metabolic cost during self-selected gait (%).
Means (SDs) of the biomechanical variables derived from both the left and right steps were computed for each individual during each condition. After normality testing using Sharipo-Wilk (
Step frequency [
Self-selected and mathematical optimal (% of self-selected) ground contact times and metabolic costs for each participant, with the third order polynomial modeled fit (
1 | 0.280 | 7,915 | 44.50 | 3.12 | 2.25 | −5.61 | 1.85 | 0.859 | 0.964 |
2 | 0.251 | 7,892 | 47.87 | −0.42 | 0.05 | 1.13 | 0.07 | 0.941 | 0.944 |
3 | 0.230 | 9,892 | 46.11 | 4.46 | 0.46 | −6.26 | 0.21 | 0.916 | 0.913 |
4 | 0.234 | 7,995 | 48.93 | 1.23 | 0.47 | −1.81 | 0.21 | 0.957 | 0.970 |
5 | 0.261 | 7,508 | 40.79 | 1.97 | 0.17 | −2.73 | 0.06 | 0.996 | 0.995 |
6 | 0.239 | 7,150 | 51.06 | −2.06 | 3.51 | 5.74 | 5.01 | 0.606 | 0.331 |
7 | 0.236 | 10,766 | 46.89 | 7.81 | 3.91 | −15.64 | 4.20 | 0.850 | 0.856 |
8 | 0.245 | 6,892 | 49.55 | 8.18 | 10.55 | −14.73 | 11.02 | 0.847 | 0.995 |
9 | 0.258 | 7,859 | 45.39 | −4.50 | 1.20 | −1.35 | 0.03 | 0.739 | 0.719 |
10 | 0.232 | 9958 | 32.26 | −3.42 | 0.72 | 0.31 | 0.01 | 0.690 | 0.576 |
The mean self-selected leg stiffness was 8.38 ± 1.33 kN·m−1, with a mathematical optimal leg stiffness identifiable for all participants. A similar amount of variance in metabolic cost could be explained by leg stiffness (
Half of the participants (
Mathematically optimal ground contact time [ms;
Relationship between the deviations from the self-selected running gait characteristics (%) and from the metabolic cost during self-selected gait (%).
Mean unit change in ground contact time and leg stiffness for the group (
There was a medium strength, significant association between RPE and metabolic cost (
The aim of this study was to examine the effect of manipulating ground contact time on the metabolic cost of running, in addition to determining the effect of altered leg stiffness on the metabolic cost of running. It was the first study to identify that ground contact time and leg stiffness are self-optimized gait characteristics, as it was observed that trained runners are operating at, or close to, their mathematical economical optimal during submaximal running. In addition, nearly all runners (90%) were using self-selected ground contact times and leg stiffness's that produced metabolic costs within 5% of their mathematical optimal metabolic cost. These findings build upon early work by Hogberg (
In support of our first hypothesis an identifiable optimal (minimum) was observed for all runners, and a curvilinear, U-shaped relationship appeared to be present between metabolic cost, and ground contact time and leg stiffness (example data
Ground contact time appears to have a narrow optimal range that runners can operate within, inducing changes to metabolic cost with only minor alterations to ground contact time, as shown by a relatively steeper portion at the base of the curve (surrounding minimum) than leg stiffness (
During stable running with varying stride frequencies, leg stiffness adjustments also lead to the generation of a constant leg force (Farley and González,
The majority (
As gait selection and self-optimization are deemed a subconscious processes (Cavanagh and Williams,
By instructing runners to shorten or lengthen their ground contact time we were able to uniquely test the effect of ground contact time on metabolic cost, whilst constraining running velocity, step frequency/length, and thus, stride frequency/length. These constraints were important as they have known effects on metabolic cost (Gutmann et al.,
To-date only one study has altered gait characteristics toward an individual's mathematical optimal. A 3-week intervention successfully altered stride frequency toward an individual's mathematical optimal and reduced metabolic cost in three runners (Morgan et al.,
The medium strength relationship between perceived effort and metabolic cost in the current study supports our second hypothesis, but is below the criterion presented by Chen et al. (
We acknowledge that there were several limitations in this study. Even though every participant received the same cues, individual interpretations resulted in self-selected changes in ground contact time. This led to some runners producing larger increases and decreases in ground contact time than others. Whilst, we were unable to overcome this within our laboratory, we believe cueing in this manner represents a useable gait retraining strategy for coaches and practitioners. Further, due to the between-participant variation in manipulated ground contact time the analysis focused on individual responses rather than group relationships. This approach, however, allows the identification of a range of responses, which coaches and practitioners may also observe and can quantify using the free software developed (Moore,
Ground contact time and leg stiffness were shown to be self-optimized in a group of trained runners, with all runners except one being within 5% of their optimal metabolic cost during their habitual running gait. Furthermore, identifiable minima were found for all runners suggesting the presence of curvilinear, U-shaped relationship between metabolic cost, and ground contact time and leg stiffness. Runners operated within a narrower band of optimal ground contact time than leg stiffness when running velocity and step frequency were constrained. Consequently, optimal ground contact time may have greater importance for economically optimal movement criteria than leg stiffness. The majority of runners favored a slightly shorter ground contact time and higher leg stiffness than optimal, suggesting a reliance on elastic energy storage and release and that the human body may be tuned to minimize metabolic cost rather than impact-related forces. Manipulating running gait appeared to disrupt the relationship between metabolic cost and perceived effort and, therefore, coaches and practitioners are not advised to use RPE as a surrogate measure during economical running gait assessments.
The datasets generated for this study can be found in the Figshare
The studies involving human participants were reviewed and approved by Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University. The patients/participants provided their written informed consent to participate in this study.
ISM and KJA conceived and designed the study and drafted the manuscript. CC and JH recruited participants and undertook data collection. MM-R assisted with study design and data collection. ISM conducted the computational and statistical analysis. HSRJ contributed to the preparation of the manuscript. All authors provided critical insight in the final version.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The authors would like to thank Michael Long for the technical expertise he provided during the study.
The Supplementary Material for this article can be found online at: