Running economy, known as the steady-state oxygen consumption at a given submaximal intensity, has been proposed as one of the key factors differentiating East African runners from other running communities around the world. Kenyan runners have dominated middle- and long-distance running events and this phenomenon has been attributed, in part at least, to their exceptional running economy. Despite such speculation, there are no data on running mechanics during real-life situations such as during training or competition. The use of innovative wearable devices together with real-time analysis of data will represent a paradigm shift in the study of running biomechanics and could potentially help explain the outstanding performances of certain athletes. For example, the integration of foot worn inertial sensors into the training and racing of athletes will enable coaches and researchers to investigate foot mechanics (e.g., an accurate set of variables such as pitch and eversion angles, cadence, symmetry, contact and flight times or swing times) during real-life activities and facilitate feedback in real-time. The same technological approach also can be used to help the athlete, coach, sports physician, and sport scientist make better informed decisions in terms of performance and efficacy of interventions, treatments or injury prevention; a kind of “telesport” equivalent to “telemedicine.” There also is the opportunity to use this real-time technology to advance broadcasting of sporting events with the transmission of real-time performance metrics and in doing so enhance the level of entertainment, interest, and engagement of enthusiasts in the broadcast and the sport. Such technological advances that are able to unobtrusively augment personal experience and interaction, represent an unprecedented opportunity to transform the world of sport for participants, spectators, and all relevant stakeholders.

Purpose: To examine the net oxygen cost, oxygen kinetics, and kinematics of level and uphill running in elite ultratrail runners. Methods: Twelve top-level ultradistance trail runners performed two 5-min stages of treadmill running (level, 0%, men 15 km·h−1, women 13 km·h−1; uphill, 12%, men 10 km·h−1, women 9 km·h−1). Gas exchanges were measured to obtain the net oxygen cost and assess oxygen kinetics. In addition, running kinematics were recorded with inertial measurement unit motion sensors on the wrist, head, belt, and foot. Results: Relationships resulted between level and uphill running regarding oxygen uptake (), respiratory exchange ratio, net energy, and oxygen cost, as well as oxygen kinetics parameters of amplitude and time delay of the primary phase and time to reach steady state. Of interest, net oxygen cost demonstrated a significant correlation between level and uphill conditions (r = .826, P < .01). Kinematics parameters demonstrated relationships between level and uphill running, as well (including contact time, aerial time, stride frequency, and stiffness; all P < .01). Conclusion: This study indicated strong relationships between level and uphill values of net oxygen cost, the time constant of the primary phase of oxygen kinetics, and biomechanical parameters of contact and aerial time, stride frequency, and stiffness in elite mountain ultratrail runners. The results show that these top-level athletes are specially trained for uphill locomotion at the expense of their level running performance and suggest that uphill running is of utmost importance for success in mountain ultratrail races.

Ski Mountaineering (SkiMo) is a fast growing sport requiring both endurance and technical skills. It involves different types of locomotion with and without the skis. The aim of this study is to develop and validate in the snowfield a novel inertial-based system for analysing cycle parameters and classifying movement in SkiMo in real-time. The study was divided into two parts, one focused on real-time parameters estimation (cadence, distance from strides, stride duration, stride length, number of strides, slope gradient, and power) and, second, on transition detection (kickturns, skin on, skin off, ski on and off backpack) in order to classify between the different types of locomotion. Experimental protocol involved 16 experienced subjects who performed different SkiMo trials with their own equipment instrumented with a ski-mounted inertial sensor. The results obtained by the algorithm showed precise results with a relative error near 5% on all parameters. The developed system can, therefore, be used by skiers to obtain quantitative training data analysis and real-time feedback in the field. Nevertheless, a deeper validation of this algorithm might be necessary in order to confirm the accuracy on a wider population of subjects with various skill levels.

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