Kenneth P. Clark

Are running speeds maximized with simple-spring stance mechanics?

Are the fastest running speeds achieved using the simple-spring stance mechanics predicted by the classic spring-mass model? We hypothesized that a passive, linear-spring model would not account for the running mechanics that maximize ground force application and speed. We tested this hypothesis by comparing patterns of ground force application across athletic specialization (competitive sprinters vs. athlete nonsprinters, n = 7 each) and running speed (top speeds vs. slower ones). Vertical ground reaction forces at 5.0 and 7.0 m/s, and individual top speeds (n = 797 total footfalls) were acquired while subjects ran on a custom, high-speed force treadmill. The goodness of fit between measured vertical force vs. time waveform patterns and the patterns predicted by the spring-mass model were assessed using the R(2) statistic (where an R(2) of 1.00 = perfect fit). As hypothesized, the force application patterns of the competitive sprinters deviated significantly more from the simple-spring pattern than those of the athlete, nonsprinters across the three test speeds (R(2) <0.85 vs. R(2) ≥ 0.91, respectively), and deviated most at top speed (R(2) = 0.78 ± 0.02). Sprinters attained faster top speeds than nonsprinters (10.4 ± 0.3 vs. 8.7 ± 0.3 m/s) by applying greater vertical forces during the first half (2.65 ± 0.05 vs. 2.21 ± 0.05 body wt), but not the second half (1.71 ± 0.04 vs. 1.73 ± 0.04 body wt) of the stance phase. We conclude that a passive, simple-spring model has limited application to sprint running performance because the swiftest runners use an asymmetrical pattern of force application to maximize ground reaction forces and attain faster speeds.

Foot speed, foot-strike and footwear: linking gait mechanics and running ground reaction forces

Running performance, energy requirements and musculoskeletal stresses are directly related to the action–reaction forces between the limb and the ground. For human runners, the force–time patterns from individual footfalls can vary considerably across speed, foot-strike and footwear conditions. Here, we used four human footfalls with distinctly different vertical force–time waveform patterns to evaluate whether a basic mechanical model might explain all of them. Our model partitions the bodys total mass (1.0Mb) into two invariant mass fractions (lower limb=0.08, remaining body mass=0.92) and allows the instantaneous collisional velocities of the former to vary. The best fits achieved (R2 range=0.95–0.98, mean=0.97±0.01) indicate that the model is capable of accounting for nearly all of the variability observed in the four waveform types tested: barefoot jog, rear-foot strike run, fore-foot strike run and fore-foot strike sprint. We conclude that different running ground reaction force–time patterns may have the same mechanical basis.

A general relationship links gait mechanics and running ground reaction forces

ABSTRACT The relationship between gait mechanics and running ground reaction forces is widely regarded as complex. This viewpoint has evolved primarily via efforts to explain the rising edge of vertical force–time waveforms observed during slow human running. Existing theoretical models do provide good rising-edge fits, but require more than a dozen input variables to sum the force contributions of four or more vague components of the bodys total mass (mb). Here, we hypothesized that the force contributions of two discrete body mass components are sufficient to account for vertical ground reaction force–time waveform patterns in full (stance foot and shank, m1=0.08mb; remaining mass, m2=0.92mb). We tested this hypothesis directly by acquiring simultaneous limb motion and ground reaction force data across a broad range of running speeds (3.0–11.1 m s−1) from 42 subjects who differed in body mass (range: 43–105 kg) and foot-strike mechanics. Predicted waveforms were generated from our two-mass model using body mass and three stride-specific measures: contact time, aerial time and lower limb vertical acceleration during impact. Measured waveforms (N=500) differed in shape and varied by more than twofold in amplitude and duration. Nonetheless, the overall agreement between the 500 measured waveforms and those generated independently by the model approached unity (R2=0.95±0.04, mean±s.d.), with minimal variation across the slow, medium and fast running speeds tested (ΔR2≤0.04), and between rear-foot (R2=0.94±0.04, N=177) versus fore-foot (R2=0.95±0.04, N=323) strike mechanics. We conclude that the motion of two anatomically discrete components of the bodys mass is sufficient to explain the vertical ground reaction force–time waveform patterns observed during human running. Summary: A basic relationship that links the motion of running to the ground forces applied enables practical, motion-based predictions of force–time patterns at essentially all speeds and regardless of foot-strike mechanics.

Experience level influences the effect of attentional focus on sprint performance

Two experiments evaluated the influence of attentional focus on 10-meter sprint time and start kinetics in a group of collegiate soccer players and highly experienced sprinters. In Experiment 1, the collegiate soccer players were asked to perform 10-meter sprints under an external focus condition, an internal focus condition and a control condition. For the 10-meter sprint time, the results showed that both the external focus and control conditions resulted in significantly faster sprint times than the internal focus condition. There were no significant differences observed between the external focus and control conditions. There were also no significant differences observed across any of the conditions for a select set of kinetic variables. In Experiment 2, the highly experienced sprinters performed the same 10-meter sprint task using the same instructional conditions as in Experiment 1. For the 10-meter sprint time and kinetic variables, there were no significant differences observed across any of the conditions. These results provide new evidence that experience level mediates the influence of attentional focus on sprint performance.