Relationship Between Changes In Foot Strike Pattern And Indices Of Fatigue In A Maximal 800-Meter Run

During faster, shorter distances, such as an 800-m middle distance, it is unclear whether runners are able to maintain a FF strike pattern. The purpose of this study was to evaluate changes in FSP throughout a maximal 800-m run. Twenty-one subjects (14 female, 7 male; age: 23.86 ± 4.25 yrs) were recruited for this study from the surrounding area and university. Subjects completed a maximal effort 800-m run while FSP and muscle activity of the tibialis anterior (TA) and lateral gastrocnemius (LG) were assessed. The main results showed there was a significant increase in split times throughout the 800-meter run (F [3, 60] = 15.188, p< 0.001). There were significant differences seen between curves and straight intervals for average angular velocity of the foot (F [1, 20] = 21.707, p<0.001) and average change in foot angle (F [1, 20] = 18.445, p<0.001). FSP did not change throughout the 800-m run, with subjects remaining in a more FF strike position; however, there were significant differences in FSP between straight intervals and curve intervals, where subjects employed a more FF strike on the curve intervals compared to straight intervals

The Comparison of Treadmill and Overground Running with the Use of Inertial Measurement Units (IMUs)

Treadmill running has historically been utilized in the laboratory to mimic outdoor running. Recent developments in portable technology, such as Inertial Measurement Units (IMUs) allow researchers to assess runners in their natural environment. PURPOSE: The primary purpose of the study was to compare peak tibial acceleration, stance time, stride frequency, and rearfoot eversion velocity between treadmill and overground running (asphalt, track, and grass). The secondary purpose was to test the reliability of IMU-based estimations of maximum rearfoot eversion velocity during treadmill running. METHODS: Twenty subjects (Age: 22.1 ± 2.0 yrs, Mass: 70.8 ± 11.9 kg, Height: 174.5 ± 10.0 cm, 8F/12M) participated. After consent and a warm-up period, IMUs were placed on the anteromedial aspect of the right distal tibia (1600Hz) and the posterior heel cap of the right shoe (1125 Hz) to record 3D linear accelerations and angular velocities. Subjects then ran three 30-meter trials on each overground surface (grass, track, and asphalt) at their self-selected speed. A timing system was used to ensure the same running speed was used between conditions. Subsequently, all subjects ran on a treadmill while their rearfoot motion was recorded through high-speed videography (240 Hz). Only one continuous trial was performed for each subject on the treadmill. In each of these trials, the variables of stride frequency, tibial acceleration, maximum rearfoot eversion velocity, and stance time were collected from the IMUs. Maximum rearfoot eversion velocity was the only variable processed from the rearfoot motion video data. A total of 30 steps from each condition were extracted and analyzed (10 steps from each trial for overground surfaces). In our statistical analysis, separate repeated measures ANOVAs or Friedman tests were performed on the mean and variability of each variable, depending on data normality, to examine differences between surface conditions. Post-hoc analysis was performed when appropriate through either Fisher’s LSD (α = 0.05) or Wilcoxon signed-rank tests (α = 0.008). RESULTS: The means of stride frequency, peak tibial acceleration, and maximum rearfoot eversion velocity were significantly different (p \u3c 0.001) between surfaces. Specifically, stride frequency was the fastest during treadmill running (1.39 (0.09) strides/sec) and slowest while running on grass (1.35 (0.07) strides/sec). Peak tibial acceleration was not different between the outdoor running conditions (asphalt: 11.0 (2.7) G, grass: 10.7 (3.4) G, and track: 10.6 (2.3) G), but significantly less during treadmill running (7.9 (1.6) G). Maximum rearfoot eversion velocity was lowest on grass (394.9 (256.5) deg/s) and greatest on track (623.5 (299.3) deg/s) and asphalt (620.7 (289.1) deg/sec). There was no difference in stance time between surfaces (p = 0.231). The variabilities of stance time, stride frequency, maximum rearfoot eversion velocity, and peak tibial acceleration were all found to be significantly different (p \u3c 0.001) between running surface. Treadmill running presented with the lowest levels of variability in stride frequency (0.016 (0.005) strides/sec), maximum rearfoot eversion velocity (70.10 (35.58) deg/s), and peak tibial acceleration (0.79 (0.32) G) across all conditions. Due to this, all variables other than stance time were significantly less variable during treadmill running than any of the overground conditions. In contrast, running on grass displayed significantly larger variabilities across all conditions in stance time (0.014 (0.009) sec), stride frequency (0.029 (0.010) strides/sec), maximum rearfoot eversion velocity (123.71 (36.16) deg/s), and peak tibial acceleration (2.41 (1.21) G). Lastly, maximum rearfoot eversion velocities acquired from the heel mounted IMU obtained a moderate reliability with the high-speed videography (ICC = 0.739, CI: 0.322 – 0.899). CONCLUSION: A single IMU appears to only be moderately reliable when recording eversion velocities during running, so caution may be warranted when using a single IMU to estimate rearfoot motion during running. With the use of IMUs, treadmill running has been shown to be different than overground in terms of both mean differences and variability. As a result, laboratory-based studies on running biomechanics may not truly reflect the “natural” gait used on outdoor running surfaces

Metabolic and mechanical changes in ultra-endurance running races and the effects of a specific training on energy cost of running

The present thesis is divided into two parts. Part I: The objectives of the first part were to examine the factors affecting the ultra-endurance performance and in particular which aspects influence the cost of running (Cr). Consequently, we defined how the Cr and running mechanics changed during different types (i.e. level and uphill) of ultra-endurance races. Finally, we proposed a specific training protocol for improving the Cr in high-level ultra-marathoners. We assessed the Cr by measuring the oxygen consumption at one (or more) fixed speeds using a metabolic unit. Further, for the running mechanics measurement and the spring-mass model parameters computation we used video analysis. Other parameters such as maximal muscle power of the lower limbs (MMP), morphological properties of the gastrocnemius medialis and Achilles tendon stiffness were also measured. Our studies showed that the maximal oxygen uptake, the fraction of it maintained throughout the race and the Cr are the main physiological parameters affecting the ultra-endurance performance, both in level and uphill competitions. Moreover, low Cr values were related to high MMP, vertical stiffness (kvert), low foot print index (FPI), Achilles tendon stiffness and external work. These results indicate that MMP, kvert and FPI are important factors in determining ultra-endurance performance. Also, our studies reported that during ultra-endurance competitions athletes tend to change their running mechanics after a certain time (~4 hours) rather than after a certain distance covered. Then, by adding strength, explosive and power training to the usual endurance training it is possible to lower the cost of running leading to a better performance. From these conclusions we suggest new training protocol for the ultra-marathoners including strength, explosive and power training which maintain a correct and less expensive running technique during ultra-endurance events. Part II: The aim of the second part was to develop and validate a customized thermoplastic polyurethane insole shoe sensor for collecting data about the ground reaction forces (GRF), contact and aerial times. This prototype allowed us to collect vertical GRF and contact time by using piezoresistive force sensors (RFS). Our final model was composed by a rubber insole, five RFSs, an s-beam load cell, an acquisition device (NI myRIO) and a battery case. By using this device we can collect data on field, avoiding the restrictions imposed by the laboratory environmen

Runners as Biomechanical Systems: New Approaches with the Spring-Mass Model

Running is fundamentally a simple activity, but the physical realization of it is complex. The gait patterns of a runner are the product of ever-changing systems and interactions of biomechanical components, and as such, the study of these mechanical characteristics is challenging. Traditional methods have focused on discrete components of gait and thus struggle to contextualize observations. Systemic analyses have been limited to simple descriptive models, often with exclusive or restrictive assumptions. This dissertation sought to develop novel methods for the systemic analyses using an established canonical model of the running gait – the spring-mass model – as a template. It further sought to conduct a series of biomechanical studies using this template-based approach as a framework to interpret the observations. Specifically, a method is first presented to estimate the system-level spring-mass characteristics of a runner using nonlinear regression with only the vertical ground reaction force time series of the runner. To facilitate this method, a novel parameterized form of the sinusoidal vGRF approximation was derived and validated. This NLR-based analyses yielded leg stiffness estimates that were consistent with traditional methods and further suggested that additional systemic parameters do not behave as traditional methods assume. Next, two investigations are presented that explore this method along with new methods for spring-mass dynamics comparisons and with established methods for spring-mass parameter analysis. These investigations included a cohort comparison of elite Kenyan distance runners against a cohort of non-elite recreational runners and a paired comparison of subjects before and after an ultramarathon. It was shown that the Kenyan runners behaved more like the simple elastic system than the recreational runners and that the ultra-marathon runners demonstrated consistent systemic patterns but greater overall template dissimilarity following the race. Finally, traditional methods of spring-mass analyses were applied with a more comprehensive mixed-model experimental design to fully characterize the system-level behavior of elite middle distance runners across a spectrum of speeds. The mixed-model template-based analysis revealed that the elite runners ran as stiffer systems than their sub-elite counterparts and that their mechanical behavior was more persistent across speeds. Together, this series of investigations established and validated new methods and improved upon the implementation of existing methods with which to assess running gait holistically and analyze it as a system. It is hoped that this work will provide useful tools, new frameworks, and fresh inspiration for scientists, coaches, and athletes to assess and interpret the movements of runners.PHDKinesiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155052/1/gtburns_1.pd

The accuracy of the FitSense FS-1 speedometer for estimating distance, speed, and energy expenditure

The purpose of this study was to examine the accuracy of the FitSense FS-1 Speedometer for estimating distance, speed, and energy expenditure while walking and running at different speeds and grades. The study was divided into three experiments. Experiment I investigated the accuracy of the FitSense for estimating distance while walking and running at self-selected speeds during repeated 1600 m tests. Experiment II investigated the accuracy of the FitSense for estimating speed (vs. a handheld digital tachometer) and energy expenditure (vs. indirect calorimetry) during treadmill walking (3.0, 4.0, and 5.0 miles • hr-1) and running (5.0, 6.0, and 7.0 miles • hr-1) on a level grade. Experiment III investigated the accuracy of the FitSense for estimating energy expenditure (vs. indirect calorimetry) during treadmill walking with an increasing grade (0.0, 2.5, 5.0, 7.5, and 10.0%). Twenty-four subjects (15 male, 9 female) volunteered for Experiment I. A subset of 12 subjects (7 male, 5 female) also volunteered for Experiments II and III. For Experiment I, one-sample t-tests revealed no significant difference between actual distance and the distance estimated by the FitSense during the walking tests. A significant difference was found for distance while running (p = 0.016). During Experiment II, a significant difference was found for speed while walking on a level grade. Post-hoc pairwise comparisons found significant stage differences between 3.0 and 5.0 miles • hr-1 and 4.0 and 5.0 miles • hr-1. Paired t-tests found no significant differences between the estimated and measured speed for walking speeds of 3.0 and 4.0 miles • hr-1 or for running speeds of 5.0, 6.0, and 7.0 miles • hr-1. A significant difference between measured and estimated speed was found while walking at 5.0 miles • hr-1 (p \u3c 0.001). A repeated measures ANOVA demonstrated significant differences for energy expenditure while walking on a level grade. Post-hoc pairwise comparisons revealed significant differences between each stage while walking. Paired t-tests also found significant differences between measured and estimated energy expenditure while walking at 4.0 and 5.0 miles • hr-1,/sup\u3e. No significant differences were found for energy expenditure while running. In Experiment III, a significant difference was found for energy expenditure while walking with an increasing grade. Post-hoc pairwise comparisons revealed significant differences between each grade. Paired t-tests also found significant differences between measured and estimated energy expenditure for each grade. In conclusion, the FitSense FS-1 Speedometer is an accurate tool for estimating distance while walking and running and for estimating speed while walking at 3.0 and 4.0 miles • hr-1 and running at 6.0 and 7.0 miles • hr-1 on a level grade. However, the FitSense underestimates energy expenditure while walking and running on a level grade and with an increasing grade

A new approach to running style analysis using a pressure-sensitive insole device: a small step towards injury prevention

Running-related injuries affect about 50% of runners every year. Our running style could be a contributing factor to the occurrence of these overuse injuries. We used a pressure insole device to observe the effect of running speed on running style, to compare the running style of previously injured and uninjured recreational runners, and to observe the effect of minimalistic vs conventional running shoes. Continuous measurement allowed us to assess variability of running style from one stride to the other, which warrants further investigation in the area of running injury prevention. Prospective follow-up of runners also identified using multiple pairs of running shoes simultaneously and practicing other sports besides running as protective against sustaining an overuse injury

Analysis of the backpack loading efects on the human gait

Gait is a simple activity of daily life and one of the main abilities of the human being. Often during leisure, labour and sports activities, loads are carried over (e.g. backpack) during gait. These circumstantial loads can generate instability and increase biomechanicalstress over the human tissues and systems, especially on the locomotor, balance and postural regulation systems. According to Wearing (2006), subjects that carry a transitory or intermittent load will be able to find relatively efficient solutions to compensate its effects.info:eu-repo/semantics/publishedVersio

Biomechanical comparisons between straight and bend sprinting in athletic sprint events

During bend sprinting, the continuous need to change direction affects athletes' whole-body mechanics. Continuously changing direction results in athletes not being able to achieve the same velocities on the bend as seen during straight-line sprinting. The aim of this thesis was to identify technique and performance differences between bend and straight-line sprinting. Two studies were conducted, one empirical study with experienced bend sprinters and one scoping review synthesising the existing bend sprinting literature. No differences were found in performance, push time, or most kinetic variables when analysing the effect of the bend during block starts compared with straight-line sprinting. However, there were reductions in vertical force on the bend compared with straight-line sprinting, which may negatively impact initial steps after block exit by reducing step length. Therefore, the bend reduces performance in subsequent race phases after block exit, potentially because athletes line their blocks up straight to increase anterior velocity. The results from the scoping review found that the effectiveness of strength training, which targets the performance descriptors, lower body kinematics, and ground reaction forces, should be further explored. A focus should be how athletes can better maintain variables closer to those during straight-line sprinting. Determining which variables are closely related to performance in sprinters who have greater velocities on the bend, and sprinters who can better maintain their velocity on the bend compared with straight-line sprinting, would help improve all bend sprinters. Additionally, statistical analysis such as statistical parametric mapping would provide additional information on the characteristics of the waveform that differentiate performers that may be lost when analysing discrete variables. Finally, advancements in technology should be explored by biomechanists to capture data ecologically during training and competition. Overall, changes in performance on the bend occur post block exit. However, a decrease in vertical force may impact the first few steps by reducing step length and, therefore, velocity. Variables related to better bend sprinters need to be identified using statistical analysis such as parametric mapping and advances in technology. An intervention study could then evaluate the effectiveness of strength training targeting the performance descriptors, lower body kinematics, and ground reaction forces, providing insights into improving bend sprinting performance