Athlete-monitoring systems are commonly used in professional sport to provide insights into athlete training readiness and injury risk. 1 In the case of professional team sports such as Australian football (AF), readiness refers to a player’s ability to complete planned training activities with no
Samuel Ryan, Thomas Kempton, and Aaron J. Coutts
Heidi R. Thornton, Jace A. Delaney, Grant M. Duthie, and Ben J. Dascombe
Athlete-monitoring data provide useful information as to whether athletes are responding appropriately to impose training and competition demands. 1 , 2 Evaluating training-monitoring data is crucial to ensure that athletes are exposed to sufficient training to prepare them for the requirements of
Samuel Ryan, Emidio Pacecca, Jye Tebble, Joel Hocking, Thomas Kempton, and Aaron J. Coutts
Athlete monitoring systems are commonly used in professional team sports to provide coaches and scientists with an understanding of player performance readiness and injury risk. 1 – 3 Information from these systems is used to plan training load to maximize adaptations while maintaining player
Stephen Crowcroft, Katie Slattery, Erin McCleave, and Aaron J. Coutts
parsimonious approach to guide training prescription, athlete monitoring systems are now commonly implemented in high-performance sport to assist in the decision-making process. 5 , 6 However, no research has investigated if the use of athlete monitoring tools can improve upon an experienced coach
Dan Weaving, Clive Beggs, Nicholas Dalton-Barron, Ben Jones, and Grant Abt
by practitioners to guide decision making relating to when amendments (eg, progression or regression) to training prescription should be made, depending on how “dose” and “response” change over time. Figure 1 —Example athlete-monitoring heuristic decision matrix. To analyze the change in the training
Fergus O’Connor, Heidi R. Thornton, Dean Ritchie, Jay Anderson, Lindsay Bull, Alex Rigby, Zane Leonard, Steven Stern, and Jonathan D. Bartlett
, which preceded an injury incidence, were calculated. These time courses are consistent with that of other research 12 and are common within athlete monitoring systems. While previous studies have assessed the relationship between numerous workload scenarios and injury, 18 this study focused on the
Stephen Crowcroft, Erin McCleave, Katie Slattery, and Aaron J. Coutts
To assess measurement sensitivity and diagnostic characteristics of athlete-monitoring tools to identify performance change.
Fourteen nationally competitive swimmers (11 male, 3 female; age 21.2 ± 3.2 y) recorded daily monitoring over 15 mo. The self-report group (n = 7) reported general health, energy levels, motivation, stress, recovery, soreness, and wellness. The combined group (n = 7) recorded sleep quality, perceived fatigue, total quality recovery (TQR), and heart-rate variability. The week-to-week change in mean weekly values was presented as coefficient of variance (CV%). Reliability was assessed on 3 occasions and expressed as the typical error CV%. Week-to-week change was divided by the reliability of each measure to calculate the signal-to-noise ratio. The diagnostic characteristics for both groups were assessed with receiver-operating-curve analysis, where area under the curve (AUC), Youden index, sensitivity, and specificity of measures were reported. A minimum AUC of .70 and lower confidence interval (CI) >.50 classified a “good” diagnostic tool to assess performance change.
Week-to-week variability was greater than reliability for soreness (3.1), general health (3.0), wellness% (2.0), motivation (1.6), sleep (2.6), TQR (1.8), fatigue (1.4), R-R interval (2.5), and LnRMSSD:RR (1.3). Only general health was a “good” diagnostic tool to assess decreased performance (AUC –.70, 95% CI, .61–.80).
Many monitoring variables are sensitive to changes in fitness and fatigue. However, no single monitoring variable could discriminate performance change. As such the use of a multidimensional system that may be able to better account for variations in fitness and fatigue should be considered.
Ed Maunder, Andrew E. Kilding, Christopher J. Stevens, and Daniel J. Plews
Electro Inc, Kempele, Finland; Forerunner ® 920XT, Edge ® 520 Plus; Garmin, Schaffhausen, Switzerland). Athlete Monitoring Well-being was assessed via 5-point Likert scales of fatigue, sleep quality, muscle soreness, stress, and mood. These scores were summed to measure global well-being. 14 Scales
Sam Coad, Bon Gray, and Christopher McLellan
To assess match-to-match variations in salivary immunoglobulin A concentration ([s-IgA]) measured at 36 h postmatch throughout an Australian Football League (AFL) premiership season and to assess the trends between 36-h-postmatch [s-IgA] and match-play exercise workloads throughout the same season.
Eighteen elite male AFL athletes (24 ± 4.2 y, 187.0 ± 7.1 cm, 87.0 ± 7.6 kg) were monitored on a weekly basis to determine total match-play exercise workloads and 36-h-postmatch [s-IgA] throughout 16 consecutive matches in an AFL premiership season. Global positioning systems (GPS) with integrated triaxial accelerometers were used to measure exercise workloads (PlayerLoad) during each AFL match. A linear mixed-model analyses was conducted for time-dependent changes in [s-IgA] and player load.
A significant main effect was found for longitudinal postmatch [s-IgA] data (F 16,240 = 3.78, P < .01) and PlayerLoad data (F 16,66 = 1.98, P = .03). For all matches after and including match 7, a substantial suppression trend in [s-IgA] 36-h-postmatch values was found compared with preseason baseline [s-IgA].
The current study provides novel data regarding longitudinal trends in 36-h-postmatch [s-IgA] for AFL athletes. Results demonstrate that weekly in-season AFL match-play exercise workloads may result in delayed mucosal immunological recovery beyond 36 h postmatch. The inclusion of individual athlete-monitoring strategies of [s-IgA] may be advantageous in the detection of compromised postmatch mucosal immunological function for AFL athletes.
Rob Gathercole, Ben Sporer, Trent Stellingwerff, and Gord Sleivert
To examine the reliability and magnitude of change after fatiguing exercise in the countermovement-jump (CMJ) test and determine its suitability for the assessment of fatigue-induced changes in neuromuscular (NM) function. A secondary aim was to examine the usefulness of a set of alternative CMJ variables (CMJ-ALT) related to CMJ mechanics.
Eleven male college-level team-sport athletes performed 6 CMJ trials on 6 occasions. A total of 22 variables, 16 typical (CMJ-TYP) and 6 CMJ-ALT, were examined. CMJ reproducibility (coefficient of variation; CV) was examined on participants’ first 3 visits. The next 3 visits (at 0, 24, and 72 h postexercise) followed a fatiguing high-intensity intermittent-exercise running protocol. Meaningful differences in CMJ performance were examined through effect sizes (ES) and comparisons to interday CV.
Most CMJ variables exhibited intraday (n = 20) and interday (n = 21) CVs of <10%. ESs ranging from trivial to moderate were observed in 18 variables at 0 h (immediately postfatigue). Mean power, peak velocity, flight time, force at zero velocity, and area under the force–velocity trace showed changes greater than the CV in most individuals. At 24 h, most variables displayed trends toward a return to baseline. At 72 h, small increases were observed in time-related CMJ variables, with mean changes also greater than the CV.
The CMJ test appears a suitable athlete-monitoring method for NM-fatigue detection. However, the current approach (ie, CMJ-TYP) may overlook a number of key fatigue-related changes, and so practitioners are advised to also adopt variables that reflect the NM strategy used.