Although these measures are valuable metrics for identifying levels of stress and fatigue, invasive tests are costly, time consuming, and impractical in the applied setting ( 16,17,35,42). By contrast, internal stress describes the physiological stress associated with training and includes hormonal, immunological, and biochemical markers ( 1). External load refers to the mechanical or locomotive stress generated by an athlete and is monitored by the use of wearable devices such as GPS (Global Positioning Systems) units and accelerometers ( 14,27,29,38,47).
Multiple strategies are used to track the biomechanical and physiologic stress by quantifying external load and internal stress levels of athletes ( 17,34). Readiness refers to the current functional state that determines the athlete's ability to achieve their desired performance potential ( 32). Thus, understanding an athlete's position on the recovery-adaptation continuum is important for designing training programs that minimize the risk of maladaptation and improve performance readiness ( 12,31,45). Indeed, temporary decrements in performance generated by functional-overreaching results in supercompensation and enhanced performance ( 31). Identifying and quantifying LFF is important because elite athletes require aggressive training regimens comprising strategically induced fatigue to stimulate adaptation ( 10). This decline in performance over time has been associated with the observation that some athletes perform frequent multidimensional low-frequency fatigue (LFF) activities that are characterized by high-intensity, moderate-to-high force repetitive eccentric or stretch-shortening cycle contractions ( 10,22,24). Fatigue can be defined as the reduction in the ability of muscles to perform work resulting in a decrement in performance ( 2,28,42). Recent advances in athlete tracking and monitoring technology allow for the assessment of fatigue before and during practice and may promote the development of training strategies to optimize performance ( 11,12,17,34). Managing optimal performance in elite athletes is difficult. We conclude that Omegawave and Catapult technologies provide independent information related to performance and may be effective tools for monitoring athlete performance. 65.56 ± 20.0 minutes p = < 0.001) despite no differences in Omegawave CNS Readiness scores. An increased PL during the previous exposure was associated with decreased CMJ (58.7 ± 4.7 cm vs. The results indicated that high, compared to low CNS Omegawave Readiness Scores (6.7 ± 05.1, 4.5 ± 1.2 AU p < 0.001), were associated with increased CMJ (62.1 ± 6.5 vs. The external load that accumulated during the previous practice, quantified by PlayerLoad (PL Catapult), was compared with CMJ values and Omegawave scores. Internal stress was assessed using Omegawave Technology readiness scores and compared with the performance index of countermovement jump (CMJ). A retrospective analysis was performed on data collected over the course of the preseason in 10 elite male NCAA Division 1 basketball players. The purpose of the present study was to assess the efficacy of external load and internal stress monitoring as assessment tools for examining a performance index of fatigue.
J Strength Cond Res 32(5): 1280–1287, 2018-Few data exist that assess athlete tracking and monitoring for the development of strategies to optimize performance and reduce fatigue in elite athletes. Noninvasive assessment of internal and external player load: implications for optimizing athletic performance.
Heishman, AD, Curtis, MA, Saliba, E, Hornett, RJ, Malin, SK, and Weltman, AL.
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