In sport, high training load required to reach peak performance pushes human adaptation to their limits. In that process, athletes may experience general fatigue, impaired performance, and may be identified as overreached (OR). When this state lasts for several months, an overtraining syndrome is diagnosed (OT). Until now, no variable per se can detect OR, a requirement to prevent the transition from OR to OT. It encouraged us to further investigate OR using a multivariate approach, including physiological, biomechanical, cognitive, and perceptive monitoring. Twenty-four highly trained triathletes were separated into an overload group and a normo-trained group (NT) during 3 wk of training. Given the decrement of their running performance, 11 triathletes were diagnosed as OR after this period. A discriminant analysis showed that the changes of eight parameters measured during a maximal incremental test could explain 98.2% of the OR state (lactatemia, heart rate, biomechanical parameters and effort perception). Variations in heart rate and lactatemia were the two most discriminating factors. When the multifactorial analysis was restricted to these variables, the classification score reached 89.5%. Catecholamines and creatine kinase concentrations at rest did not change significantly in both groups. Running pattern was preserved and cognitive performance decrement was observed only at exhaustion in OR subjects. This study showed that monitoring various variables is required to prevent the transition between NT and OR. It emphasized that an OR index, which combines heart rate and blood lactate concentration changes after a strenuous training period, could be helpful to routinely detect OR.

The purpose of this study was to identify consistent features in the signals supplied by a single inertial measurement unit (IMU), or thereof derived, for the identification of foot-strike and foot-off instants of time and for the estimation of stance and stride duration during the maintenance phase of sprint running. Maximal sprint runs were performed on tartan tracks by five amateur and six elite athletes, and durations derived from the IMU data were validated using force platforms and a high-speed video camera, respectively, for the two groups. The IMU was positioned on the lower back trunk (L1 level) of each athlete. The magnitudes of the acceleration and angular velocity vectors measured by the IMU, as well as their wavelet-mediated first and second derivatives were computed, and features related to foot-strike and foot-off events sought. No consistent features were found on the acceleration signal or on its first and second derivatives. Conversely, the foot-strike and foot-off events could be identified from features exhibited by the second derivative of the angular velocity magnitude. An average absolute difference of 0.005 s was found between IMU and reference estimates, for both stance and stride duration and for both amateur and elite athletes. The 95% limits of agreement of this difference were less than 0.025 s. The results proved that a single, trunk-mounted IMU is suitable to estimate stance and stride duration during sprint running, providing the opportunity to collect information in the field, without constraining or limiting athletes' and coaches' activities.

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Biomechanical analyses using synchronized tools [electromyography (EMG), motion capture, force sensors, force platform, and digital camera] are classically performed in a laboratory environment that could influence the performance. We present a system for studying the running sprint start that synchronizes motion capture, EMG, and ground reaction force data. To maximize motion capture (Vicon 612 with six cameras), a special dim environment was created in the stadium. "Classical" tools were combined with "purpose-built" tools intended to analyse the different aspects of movement. For example, a synchronization system was built to create a common time-base for all data recordings and a portable EMG system was synchronized by a cable that was "disconnected" by the athlete's movement out of the blocks. This disconnection represented an independent event recorded by different tools. A "gap" was measured for some sprint start events between kinetic and kinematic (motion capture) data. Calibration results, measurements of time "gap", and duration of the independent event were used to validate the accuracy of motion capture and the synchronization system. The results validate the entire experimental set-up and suggest adjustment values for motion capture data. This environment can be used to study other movements and can easily be applied to several sports.

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