Purpose: To establish the interunit reliability of a range of global positioning system (GPS)-derived movement indicators, to determine the variation between manufacturers, and to investigate the difference between software-derived and raw data. Methods: A range of movement variables were obtained from 27 GPS units from 3 manufacturers (GPSports EVO, 10 Hz, n = 10; STATSports Apex, 10 Hz, n = 10; and Catapult S5, 10 Hz, n = 7) that measured the same team-sport simulation session while positioned on a sled. The interunit reliability was determined using the coefficient of variation (%) and 90% confidence limits, whereas between-manufacturers comparisons and comparisons of software versus raw processed data were established using standardized effect sizes and 90% confidence limits. Results: The interunit reliability for both software and raw processed data ranged from good to poor (coefficient of variation = 0.2%; ±1.5% to 78.2%; ±1.5%), with distance, speed, and maximal speed exhibiting the best reliability. There were substantial differences between manufacturers, particularly for threshold-based acceleration and deceleration variables (effect sizes; ±90% confidence limits: −2.0; ±0.1 to 1.9; ±0.1), and there were substantial differences between data-processing methods for a range of movement indicators. Conclusions: The interunit reliability of most movement indicators was deemed as good regardless of processing method, suggesting that practitioners can have confidence within systems. Standardized data-processing methods are recommended, due to the large differences between data outputs from various manufacturer-derived software.
Heidi R. Thornton, André R. Nelson, Jace A. Delaney, Fabio R. Serpiello, and Grant M. Duthie
Lee Taylor, Christopher J. Stevens, Heidi R. Thornton, Nick Poulos, and Bryna C.R. Chrismas
Purpose: To determine how a cooling vest worn during a warm-up could influence selected performance (countermovement jump [CMJ]), physical (global positioning system [GPS] metrics), and psychophysiological (body temperature and perceptual) variables. Methods : In a randomized, crossover design, 12 elite male World Rugby Sevens Series athletes completed an outdoor (wet bulb globe temperature 23–27°C) match-specific externally valid 30-min warm-up wearing a phase-change cooling vest (VEST) and without (CONTROL), on separate occasions 7 d apart. CMJ was assessed before and after the warm-up, with GPS indices and heart rate monitored during the warm-ups, while core temperature (T c; ingestible telemetric pill; n = 6) was recorded throughout the experimental period. Measures of thermal sensation (TS) and thermal comfort (TC) was obtained pre-warm-up and post-warm-up, with rating of perceived exertion (RPE) taken post-warm-ups. Results: Athletes in VEST had a lower ΔT c (mean [SD]: VEST = 1.3°C [0.1°C]; CONTROL = 2.0°C [0.2°C]) from pre-warm-up to post-warm-up (effect size; ±90% confidence limit: −1.54; ±0.62) and T c peak (mean [SD]: VEST = 37.8°C [0.3°C]; CONTROL = 38.5°C [0.3°C]) at the end of the warm-up (−1.59; ±0.64) compared with CONTROL. Athletes in VEST demonstrated a decrease in ΔTS (−1.59; ±0.72) and ΔTC (−1.63; ±0.73) pre-warm-up to post-warm-up, with a lower RPE post-warm-up (−1.01; ±0.46) than CONTROL. Changes in CMJ and GPS indices were trivial between conditions (effect size < 0.2). Conclusions: Wearing the vest prior to and during a warm-up can elicit favorable alterations in physiological (T c) and perceptual (TS, TC, and RPE) warm-up responses, without compromising the utilized warm-up characteristics or physical-performance measures.
Heidi R. Thornton, Jace A. Delaney, Grant M. Duthie, and Ben J. Dascombe
To investigate the ability of various internal and external training-load (TL) monitoring measures to predict injury incidence among positional groups in professional rugby league athletes.
TL and injury data were collected across 3 seasons (2013–2015) from 25 players competing in National Rugby League competition. Daily TL data were included in the analysis, including session rating of perceived exertion (sRPE-TL), total distance (TD), high-speed-running distance (>5 m/s), and high-metabolic-power distance (HPD; >20 W/kg). Rolling sums were calculated, nontraining days were removed, and athletes’ corresponding injury status was marked as “available” or “unavailable.” Linear (generalized estimating equations) and nonlinear (random forest; RF) statistical methods were adopted.
Injury risk factors varied according to positional group. For adjustables, the TL variables associated most highly with injury were 7-d TD and 7-d HPD, whereas for hit-up forwards they were sRPE-TL ratio and 14-d TD. For outside backs, 21- and 28-d sRPE-TL were identified, and for wide-running forwards, sRPE-TL ratio. The individual RF models showed that the importance of the TL variables in injury incidence varied between athletes.
Differences in risk factors were recognized between positional groups and individual athletes, likely due to varied physiological capacities and physical demands. Furthermore, these results suggest that robust machine-learning techniques can appropriately monitor injury risk in professional team-sport athletes.
Heidi R. Thornton, Jace A. Delaney, Grant M. Duthie, and Ben J. Dascombe
In professional team sports, the collection and analysis of athlete-monitoring data are common practice, with the aim of assessing fatigue and subsequent adaptation responses, examining performance potential, and minimizing the risk of injury and/or illness. Athlete-monitoring systems should be underpinned by appropriate data analysis and interpretation, to enable the rapid reporting of simple and scientifically valid feedback. Using the correct scientific and statistical approaches can improve the confidence of decisions made from athlete-monitoring data. However, little research has discussed and proposed an outline of the process involved in the planning, development, analysis, and interpretation of athlete-monitoring systems. This review discusses a range of methods often employed to analyze athlete-monitoring data to facilitate and inform decision-making processes. There is a wide range of analytical methods and tools that practitioners may employ in athlete-monitoring systems, as well as several factors that should be considered when collecting these data, methods of determining meaningful changes, and various data-visualization approaches. Underpinning a successful athlete-monitoring system is the ability of practitioners to communicate and present important information to coaches, ultimately resulting in enhanced athletic performance.
Tannath J. Scott, Heidi R. Thornton, Macfarlane T.U. Scott, Ben J. Dascombe, and Grant M. Duthie
Purpose: To compare relative and absolute speed and metabolic thresholds for quantifying match output in elite rugby league. Methods: Twenty-six professional players competing in the National Rugby League were monitored with global positioning systems (GPS) across a rugby-league season. Absolute speed (moderate-intensity running [MIRTh > 3.6 m/s] and high-intensity running [HIRTh > 5.2 m/s]) and metabolic (>20 W/kg) thresholds were compared with individualized ventilatory (first [VT1IFT] and second [VT2IFT]) thresholds estimated from the 30-15 Intermittent Fitness Test (30-15IFT), as well as the metabolic threshold associated with VT2IFT (HPmetVT2), to examine difference in match-play demands. Results: VT2IFT mean values represent 146%, 138%, 167%, and 144% increases in the HIR dose across adjustables, edge forwards, middle forwards, and outside backs, respectively. Distance covered above VT2IFT was almost certainly greater (ES range = 0.79–1.03) than absolute thresholds across all positions. Trivial to small differences were observed between VT1IFT and MIRTh, while small to moderate differences were reported between HPmetVT2 and HPmetTh. Conclusions: These results reveal that the speed at which players begin to run at higher intensities depends on individual capacities and attributes. As such, using absolute HIR speed thresholds underestimates the physical HIR load. Moreover, absolute MIR and high metabolic thresholds may over- or underestimate the work undertaken above these thresholds depending on the respective fitness of the individual. Therefore, using relative thresholds enables better prescription and monitoring of external training loads based on measured individual physical capacities.
Jace A. Delaney, Heidi R. Thornton, Grant M. Duthie, and Ben J. Dascombe
Rugby league coaches adopt replacement strategies for their interchange players to maximize running intensity; however, it is important to understand the factors that may influence match performance.
To assess the independent factors affecting running intensity sustained by interchange players during professional rugby league.
Global positioning system (GPS) data were collected from all interchanged players (starters and nonstarters) in a professional rugby league squad across 24 matches of a National Rugby League season. A multilevel mixed-model approach was employed to establish the effect of various technical (attacking and defensive involvements), temporal (bout duration, time in possession, etc), and situational (season phase, recovery cycle, etc) factors on the relative distance covered and average metabolic power (Pmet) during competition. Significant effects were standardized using correlation coefficients, and the likelihood of the effect was described using magnitude-based inferences.
Superior intermittent running ability resulted in very likely large increases in both relative distance and Pmet. As the length of a bout increased, both measures of running intensity exhibited a small decrease. There were at least likely small increases in running intensity for matches played after short recovery cycles and against strong opposition. During a bout, the number of collision-based involvements increased running intensity, whereas time in possession and ball time out of play decreased demands.
These data demonstrate a complex interaction of individual- and match-based factors that require consideration when developing interchange strategies, and the manipulation of training loads during shorter recovery periods and against stronger opponents may be beneficial.
Heidi R. Thornton, Jace A. Delaney, Grant M. Duthie, and Ben J. Dascombe
Purpose: To investigate the influence of daily and exponentially weighted moving training loads on subsequent nighttime sleep. Methods: Sleep of 14 professional rugby league athletes competing in the National Rugby League was recorded using wristwatch actigraphy. Physical demands were quantified using GPS technology, including total distance, high-speed distance, acceleration/deceleration load (SumAccDec; AU), and session rating of perceived exertion (AU). Linear mixed models determined effects of acute (daily) and subacute (3- and 7-d) exponentially weighted moving averages (EWMA) on sleep. Results: Higher daily SumAccDec was associated with increased sleep efficiency (effect-size correlation; ES = 0.15; ±0.09) and sleep duration (ES = 0.12; ±0.09). Greater 3-d EWMA SumAccDec was associated with increased sleep efficiency (ES = 0.14; ±0.09) and an earlier bedtime (ES = 0.14; ±0.09). An increase in 7-d EWMA SumAccDec was associated with heightened sleep efficiency (ES = 0.15; ±0.09) and earlier bedtimes (ES = 0.15; ±0.09). Conclusions: The direction of the associations between training loads and sleep varied, but the strongest relationships showed that higher training loads increased various measures of sleep. Practitioners should be aware of the increased requirement for sleep during intensified training periods, using this information in the planning and implementation of training and individualized recovery modalities.
Jace A. Delaney, Grant M. Duthie, Heidi R. Thornton, Tannath J. Scott, David Gay, and Ben J. Dascombe
Rugby league involves frequent periods of high-intensity running including acceleration and deceleration efforts, often occurring at low speeds.
To quantify the energetic cost of running and acceleration efforts during rugby league competition to aid in prescription and monitoring of training.
Global positioning system (GPS) data were collected from 37 professional rugby league players across 2 seasons. Peak values for relative distance, average acceleration/deceleration, and metabolic power (Pmet) were calculated for 10 different moving-average durations (1–10 min) for each position. A mixed-effects model was used to assess the effect of position for each duration, and individual comparisons were made using a magnitude-based-inference network.
There were almost certainly large differences in relative distance and Pmet between the 10-min window and all moving averages <5 min in duration (ES = 1.21–1.88). Fullbacks, halves, and hookers covered greater relative distances than outside backs, edge forwards, and middle forwards for moving averages lasting 2–10 min. Acceleration/deceleration demands were greatest in hookers and halves compared with fullbacks, middle forwards, and outside backs. Pmet was greatest in hookers, halves, and fullbacks compared with middle forwards and outside backs.
Competition running intensities varied by both position and moving-average duration. Hookers exhibited the greatest Pmet of all positions, due to high involvement in both attack and defense. Fullbacks also reached high Pmet, possibly due to a greater absolute volume of running. This study provides coaches with match data that can be used for the prescription and monitoring of specific training drills.
Farhan Juhari, Dean Ritchie, Fergus O’Connor, Nathan Pitchford, Matthew Weston, Heidi R. Thornton, and Jonathan D. Bartlett
Context: Team-sport training requires the daily manipulation of intensity, duration, and frequency, with preseason training focusing on meeting the demands of in-season competition and training on maintaining fitness. Purpose: To provide information about daily training in Australian football (AF), this study aimed to quantify session intensity, duration, and intensity distribution across different stages of an entire season. Methods: Intensity (session ratings of perceived exertion; CR-10 scale) and duration were collected from 45 professional male AF players for every training session and game. Each session’s rating of perceived exertion was categorized into a corresponding intensity zone, low (<4.0 arbitrary units), moderate (≥4.0 and <7.0), and high (≥7.0), to categorize session intensity. Linear mixed models were constructed to estimate session duration, intensity, and distribution between the 3 preseason and 4 in-season periods. Effects were assessed using linear mixed models and magnitude-based inferences. Results: The distribution of the mean session intensity across the season was 29% low intensity, 57% moderate intensity, and 14% high intensity. While 96% of games were high intensity, 44% and 49% of skills training sessions were low intensity and moderate intensity, respectively. Running had the highest proportion of high-intensity training sessions (27%). Preseason displayed higher training-session intensity (effect size [ES] = 0.29–0.91) and duration (ES = 0.33–1.44), while in-season game intensity (ES = 0.31–0.51) and duration (ES = 0.51–0.82) were higher. Conclusions: By using a cost-effective monitoring tool, this study provides information about the intensity, duration, and intensity distribution of all training types across different phases of a season, thus allowing a greater understanding of the training and competition demands of Australian footballers.
Jace A. Delaney, Heidi R. Thornton, John F. Pryor, Andrew M. Stewart, Ben J. Dascombe, and Grant M. Duthie
To quantify the duration and position-specific peak running intensities of international rugby union for the prescription and monitoring of specific training methodologies.
Global positioning systems (GPS) were used to assess the activity profile of 67 elite-level rugby union players from 2 nations across 33 international matches. A moving-average approach was used to identify the peak relative distance (m/min), average acceleration/deceleration (AveAcc; m/s2), and average metabolic power (Pmet) for a range of durations (1–10 min). Differences between positions and durations were described using a magnitude-based network.
Peak running intensity increased as the length of the moving average decreased. There were likely small to moderate increases in relative distance and AveAcc for outside backs, halfbacks, and loose forwards compared with the tight 5 group across all moving-average durations (effect size [ES] = 0.27–1.00). Pmet demands were at least likely greater for outside backs and halfbacks than for the tight 5 (ES = 0.86–0.99). Halfbacks demonstrated the greatest relative distance and Pmet outputs but were similar to outside backs and loose forwards in AveAcc demands.
The current study has presented a framework to describe the peak running intensities achieved during international rugby competition by position, which are considerably higher than previously reported whole-period averages. These data provide further knowledge of the peak activity profiles of international rugby competition, and this information can be used to assist coaches and practitioners in adequately preparing athletes for the most demanding periods of play.