Noninvasive Assessment of Internal and External Player Load: Implications for Optimizing Athletic Performance

Abstract

Heishman, AD, Curtis, MA, Saliba, E, Hornett, RJ, Malin, SK, and Weltman, AL. Noninvasive assessment of internal and external player load: implications for optimizing athletic performance. 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. 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. A retrospective analysis was performed on data collected over the course of the preseason in 10 elite male NCAA Division 1 basketball players. Internal stress was assessed using Omegawave Technology readiness scores and compared with the performance index of countermovement jump (CMJ). The external load that accumulated during the previous practice, quantified by PlayerLoad (PL; Catapult), was compared with CMJ values and Omegawave scores. 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. 59.4 ± 6.6 cm; p = 0.05), Power (6,590 ± 526.7 vs. 6,383.5 ± 606.8 W; p = 0.05), Omegawave Overall Readiness (5.8 ± 1.1 vs. 5.0 ± 0.7 AU; p = 0.05), and Omega Potential (Omega) (21.3 ± 6.3 vs. 9.9 ± 20.8 mV; p = 0.07). An increased PL during the previous exposure was associated with decreased CMJ (58.7 ± 4.7 cm vs. 60.4 ± 5.1 cm; p < 0.001) and increased TRIMP (135.1 ± 35.9 vs. 65.6 ± 20.0 AU; p < 0.001), and duration (115.4 ± 27.1 vs. 65.56 ± 20.0 minutes; p = < 0.001) despite no differences in Omegawave CNS Readiness scores. We conclude that Omegawave and Catapult technologies provide independent information related to performance and may be effective tools for monitoring athlete performance.

Training load and match-play demands in basketball based on competition level: A systematic review

“…Of the 165 articles reviewed, 35 met the criteria for the systematic review. Of the 35 articles that met the criteria, 12 had participants for elite competition demands [4,5,6,11,12,13,14,15,16,7,9,30,32], 16 articles had participants for elite training load [2,10,3,12,15,35,37,38,39,41,20,42,25,27,43,47], 6 for sub-elite competition demands [4,11,13,21,26,32], 3 for sub-elite training load [23,44,48], 5 for youth competition demands [11,18,22,9,28] and 1 for youth training load [24]. A full view of the search and selection process can be found in the PRISMA flow diagram [45] in Fig 2.…”

“…In a related topic, tracking training load in this team-sport may be of extreme importance to ensure that the players are physically prepared for competition demands from a fitness standpoint, in order to avoid acute spikes in load from a fatigue and injury prevention perspective [3,11,7,17] and to provide individualized recovery strategies [33,34]. With this in mind, a copious amount of research has also been focused on investigating and describing basketball training load parameters over recent years [35,36,37,38,39,40,41,21,42,24,43,44]. As previously mentioned for competition, accelerometry is becoming an increasingly popular means of quantifying load during training [36,38,40,21]; however, no conclusive data has been reported throughout the different studies.…”

“…With this in mind, a copious amount of research has also been focused on investigating and describing basketball training load parameters over recent years [35,36,37,38,39,40,41,21,42,24,43,44]. As previously mentioned for competition, accelerometry is becoming an increasingly popular means of quantifying load during training [36,38,40,21]; however, no conclusive data has been reported throughout the different studies. For this reason, a more in-depth and systematic analysis of the literature is warranted.…”

“…Regarding internal load, HR and session rate of perceived exertion (sRPE) (i.e., the subjective feedback from the player on a 1-10 scale multiplied by duration of training) have been shown to be a cost-effective way of providing valuable information widely used by coaches and sport scientists [35,37,41]. Remarkably, an important variability has been reported within basketball training loads based on quantification means of training load, position, perceived exertion, skill level, and training age [36,37,38,39,40,41,43,44], once again identifying the need for a systematic review of the published data.…”

Associations Between Two Athlete Monitoring Systems Used to Quantify External Training Loads in Basketball Players

“…PlayerLoad (PL), expressed as the square root of the sum of the squared instantaneous rate of change in acceleration in each of the three orthogonal planes divided by the scaling factor 100 and expressed in arbitrary units (au), is accepted as a valid and reliable parameter [11][12][13][14], and is frequently reported as the primary workload variable captured by the IMU. Indeed, previous literature has illuminated increases in PL with subsequent change in neuromuscular performance, specifically in basketball athletes [15,16]. Additionally, PL can be broken down into individual vectors (mediolateral, anteroposterior, and vertical), which has been speculated as useful in characterizing the movement signature an athlete experiences [17][18][19].…”