Rearfoot and forefoot footfall patterns: implications for barefoot running

Approximately 75-80% of runners initiate contact with the running surface on their heel and thus have a rearfoot or heel-toe footfall pattern. The remaining 20-25% initially contact the ground with the foot flat with a subsequent heel contact (midfoot pattern) or on their forefoot with no heel contact (forefoot pattern). It is unclear why different footfall patterns exist or why some runners naturally use different patterns. Some contemporary training programs advocate the adoption of a mid- or forefoot footfall pattern but there is little scientific evidence that a particular strike pattern is more efficient or less injury-prone than other patterns. In this presentation, several studies that investigated differences among the footfall patterns relative to oxygen consumption, impact characteristics, surface alterations and lower extremity coordination will be presented. In addition, two modeling studies will also be discussed. One study will determine, using optimization techniques, the passive and active characteristics on the triceps surae. The other study is a forward dynamics study with different cost functions describing the different footfall patterns. Our basic premise in these studies is that different footfall patterns serve different functional roles in human running: a heel- or midfoot strike is used for endurance running, and a forefoot strike is used for sprinting. We propose that one’s footfall pattern is an intrinsic dynamic and thus difficult to alter. However, the change from shod to barefoot running often requires an alteration in footfall pattern that may ultimately lead to injury

THE EVOLUTION OF ATHLETIC FOOTWEAR

The purpose of this presentation is to discuss the evolution of athletic footwear and how biomechanics has influenced this evolution. Footwear has undergone a significant evolution from the Paleolithic period to modern times. The origins of footwear emphasized protection from the environment. During the Egyptian, Greek and Roman eras, the need for military shoes drove the development of footwear. It was not until the 19th century that specific footwear for athletic performance was designed. Footwear were improved significantly during the first half of the 20th century but it was not until the latter portion of this century that biomechanics truly had an influence on footwear design. The intersection of biomechanics, injury risk factors and footwear development paralleled the growth in lower extremity research. More recently, the interest in barefoot running has driven the development of minimalist footwear

KNEE POWER IN LOW BACK PAIN SUBJECTS DURING RUNNING

The purpose of this study was to examine lower extremity shock absorption between runners with and without low back pain. We compared data from three groups based on low back pain status: current low back pain, resolved pain after a single bout of low back pain and runners who never had low back pain (CTRL). All subjects ran at least 20 km per week and ran on a force treadmill at 3.8 m•s-1 while kinematic and kinetic data were collected. Work was determined from joint power histories during the shock attenuation portion of the stance phase. Individuals with a history of low back pain exhibited less peak knee negative power and negative work suggesting that they exhibited decreased eccentric muscle activity during foot-ground impact. The results of this study suggest that decreased eccentric activity of the muscles crossing the knee joint is associated with individuals who have low back pain and, to a lesser extent, with those who have residual low back pain. We suggest that the decreased eccentric activity can result in the footground impact shock wave moving through the lower extremity with little attenuation to the low back region

ESTIMATING LOWER LIMB JOINT MOMENTS IN GAIT USING COMMON MACHINE LEARNING APPROACHES

The aim of this study was to investigate the efficacy of common machine learning algorithmic approaches to estimate lower limb joint moments during fast walking gait. Kinematic and ground reaction force data on 19 participants were captured with a force-plate and motion caption capture system. Inverse dynamics was used to calculate the right lower limb joint moments and common machine learning algorithmic approaches, such as Random Forest (RF), Linear Regression (LR), Neural Network (NN), AdaBoost (AB) and Gradient Boosting, were used to predict the corresponding joint moments using only the kinematic data. High coefficient of determination values (R2\u3e0.9) for predicting moments using random forest, neural network and AdaBoost are observed in for the ankle, knee and hip joints in frontal, sagittal and transverse planes. The other approaches had R2 values between ranged 0.71 and 0.97. This suggests that common machine learning algorithms may be a feasible approach to estimate joint moments during fast walking in a clinical setting for monitoring sport injury prevention and management

THE RELATIONSHIP BETWEEN ACHILLES TENDON DIMENSIONS AND FOOT STRIKE INDEX IN REARFOOT AND NON-REARFOOT RUNNERS

The purpose of this study was to describe the relationship between Achilles tendon dimensions and foot strike index in rearfoot and non-rearfoot runners. 107 recreational runners were divided into a group of rearfoot (n = 88) and a group of non-rearfoot runners (n = 19). Achilles tendon dimensions were measured by a combination of ultrasonography imaging and kinematic analysis. To analyse the footfall pattern, each participant performed 8 successful trials of running at their stated self-preferred endurance speed. Partial correlation was used for statistical analysis. Runners in the group of non-rearfoot runners, whose footfall pattern is more over forefoot, have a longer and thinner Achilles tendon