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Purpose: Can power law models accurately predict the peak intensities of rugby competition as a function of time? Methods: Match movement data were collected from 30 elite and 30 subelite rugby union athletes across competitive seasons, using wearable Global Navigation Satellite Systems and accelerometers. Each athlete’s peak rolling mean value of each measure (mean speed, metabolic power, and PlayerLoad™) for 8 durations between 5 seconds and 10 minutes was predicted by the duration with 4 power law (log–log) models, one for forwards and backs in each half of a typical match. Results: The log of peak exercise intensity and exercise duration (5–600 s) displayed strong linear relationships (R2 = .967–.993) across all 3 measures. Rugby backs had greater predicted intensities for shorter durations than forwards, but their intensities declined at a steeper rate as duration increased. Random prediction errors for mean speed, metabolic power, and PlayerLoad were 5% to 6%, 7% to 9%, and 8% to 10% (moderate to large), respectively, for elite players. Systematic prediction errors across the range of durations were trivial to small for elite players, underestimating intensities for shorter (5–10 s) and longer (300–600 s) durations by 2% to 4% and overestimating 20- to 120-second intensities by 2% to 3%. Random and systematic errors were slightly greater for subelites compared to elites, with ranges of 4% to 12% and 2% to 5%, respectively. Conclusions: Peak intensities of professional rugby union matches can be predicted with adequate precision (trivial to small errors) for prescribing training drills of a given duration, irrespective of playing position, match half, level of competition, or measure of exercise intensity. However, practitioners should be aware of the substantial (moderate to large) prediction errors at the level of the individual player.

Air travel is a key factor in Super Rugby and can have a negative influence on players' performance and well-being. The aim of this study was to identify the current practice to reduce the effects of air travel and to understand the rationale behind these interventions. "Travel managers" from eight Super Rugby teams were interviewed and answered a questionnaire. A qualitative description was performed to identify common themes and differences between participants' answers. To protect the privacy and identity of the participants, all data have been de-identified and represented as two fictionalised amalgams (Bob and Peter). The rationale behind each intervention appeared to be based on a mix of anecdotal, practice and, occasionally, literature, confirming that scientific findings are not always easily translatable to applied settings. Two different approaches, clinical (Bob) and holistic (Peter), were identified. Even if both characters acknowledge that travel variables are too many to control, it appears that team culture and practices are perceived as important as biological interventions in controlling the negative effects of travel on players' performance and well-being.

Walker, EJ, Aughey, RJ, McLaughlin, P, and McAinch, AJ. Seasonal change in body composition and physique of team sport athletes. J Strength Cond Res XX(X): 000-000, 2020-Body composition of team sport athletes was measured at 3 points across the preseason and competitive season. This repeated-measures study was conducted in 46 professional Australian football (AF) (age 23.8 ± 3.8 years), 26 soccer (age 22.7 ± 4.7 years), and 33 rugby union players (age 28.1 ± 4.2 years). A mixed-design analysis of variance was used to determine change across the season, and Pearson's correlation was used to determine the relationship between different measures. Anthropometry, dual-energy x-ray absorptiometry (DXA), and 3-dimensional (3D) scan technology were used in AF and soccer, whereas only DXA was used in rugby. Body mass remained unchanged for both AF and soccer with gains in lean mass (p < 0.01), from preseason to early in the competitive season. Skinfold measures declined in AF (p < 0.001) and soccer (p < 0.05) across the season, whereas DXA-measured fat mass only declined in soccer (p < 0.01). Rugby backs (p < 0.01) and forwards (p < 0.001) reduced body fat and gained lean mass from preseason to in-season with forwards having greater relative and absolute changes as measured by DXA. 3D technology did not show change across the season. Dual-energy x-ray absorptiometry body fat percent and the sum of skinfold correlation were large (r = 0.74 [p < 0.001, CI 0.67-0.81]). The greatest change in body composition occurs from the beginning of preseason to the start of competition, with changes returning to baseline levels toward the end of season. Dual-energy x-ray absorptiometry and skinfold measures were moderately correlated, providing a good alternative to track change in subcutaneous fat in AF and soccer athletes.

This study aimed to observe whether there were substantial differences in acceleration during team-sport locomotion between GNSS manufacturers. Speed and acceleration were obtained from 7 professional rugby league athletes via 2 GNSS manufacturers (GPSports EVO, 10 Hz and STATSports Apex, 10 Hz) worn together during the same training sessions (n = 13). Raw GNSS data were exported from respective proprietary software and a 1 Hz, 4th-order Butterworth filter applied, with differences in speed and acceleration calculated between manufacturers. To determine the difference in acceleration and speed, a root mean square deviation (RMSD) was used. Linear mixed models were used and no substantial differences were found between manufacturers in raw and filtered speed variables. RMSD for average acceleration (m · s−2) decreased from raw (RMSD: 1.77 ± 0.37 m · s−2) to those seen at the filtered (RMSD: 0.27 ± 0.23 m · s−2) and twice filtered (0.24 ± 0.23 m · s−2) variables. Raw average acceleration (m · s−2) was substantially higher in Apex compared to EVO (Difference (Diff); CI: −0.82; −0.84 to −0.80). Following application of the common filter there was no substantial difference between GNSS models for average acceleration (Diff; CI: −0.04; −0.04 to −0.04). Acceleration variables derived from each manufacturer’s proprietary software were substantially different.

Three-dimensional motion capture systems such as Vicon have been used to validate commercial electronic performance and tracking systems. However, three-dimensional motion capture cannot be used for large capture areas such as a full football pitch due to the need for many fragile cameras to be placed around the capture volume and a lack of suitable depth of field of those cameras. There is a need, therefore, for a hybrid testing solution for commercial electronic performance and tracking systems using highly precise three-dimensional motion capture in a small test area and a computer vision system in other areas to test for full-pitch coverage by the commercial systems. This study aimed to establish the validity of VisionKit computer vision system against three-dimensional motion capture in a stadium environment. Ten participants undertook a series of football-specific movement tasks, including a circuit, small-sided games and a 20 m sprint. There was strong agreement between VisionKit and three-dimensional motion capture across each activity undertaken. The root mean square difference for speed was 0.04 m·s−1 and for position was 0.18 m. VisionKit had strong agreement with the criterion three-dimensional motion capture system three-dimensional motion capture for football-related movements tested in stadium environments. VisionKit can thus be used to establish the concurrent validity of other electronic performance and tracking systems in circumstances where three-dimensional motion capture cannot be used.

Rowing instrumentation systems provide measures of stroke power, stroke rate, and boat velocity during rowing races, but how well these measures predict race performance has not been reported previously.Data were collected per stroke from 45 2000-m races using Peach PowerLine and OptimEye S5 GPS units. The boat classes assessed were nine male singles, eight female singles, three male pairs, and six female pairs. Random effects and residuals from general linear mixed modeling of stroke velocity adjusted for stroke power, stroke rate, and mean headwind provided measures interpreted as technical efficiency, race conditions, and stroke-velocity variability. These measures, along with mean race power, mean stroke rate, and mean headwind were then included in multiple linear regressions to predict race velocity from official race times. Effects were assessed for 2 SD changes in predictors and interpreted using interval hypothesis tests.Effects of mean race power, mean stroke rate, and mean headwind on race velocity ranged from small to extremely large and were mostly decisively substantial. Effects of technical efficiency and race conditions ranged from trivial to extremely large but were generally unclear, while stroke-velocity variability had trivial-small and mostly unclear effects. Prediction error was small to moderate and decisively substantial. Men's pairs lacked sufficient data for analysis.On-water rowing race performance can be predicted with mean race values of power, stroke rate, and headwind. Estimates from stroke data are potentially useful predictors but require impractical numbers of boats and races to reduce their uncertainty.

Purpose: Instrumentation systems are increasingly used in rowing to measure training intensity and performance but have not been validated for measures of power. In this study, the concurrent validity of Peach PowerLine (six units), Nielsen-Kellerman EmPower (five units), Weba OarPowerMeter (three units), Concept2 model D ergometer (one unit), and a custom-built reference instrumentation system (Reference System; one unit) were investigated.Methods: Eight female and seven male rowers [age, 21 ± 2.5 years; rowing experience, 7.1 ± 2.6 years, mean ± standard deviation (SD)] performed a 30-s maximal test and a 7 × 4-min incremental test once per week for 5 weeks. Power per stroke was extracted concurrently from the Reference System (via chain force and velocity), the Concept2 itself, Weba (oar shaft-based), and either Peach or EmPower (oarlock-based). Differences from the Reference System in the mean (representing potential error) and the stroke-to-stroke variability (represented by its SD) of power per stroke for each stage and device, and between-unit differences, were estimated using general linear mixed modeling and interpreted using rejection of non-substantial and substantial hypotheses.Results: Potential error in mean power was decisively substantial for all devices (Concept2, –11 to –15%; Peach, −7.9 to −17%; EmPower, −32 to −48%; and Weba, −7.9 to −16%). Between-unit differences (as SD) in mean power lacked statistical precision but were substantial and consistent across stages (Peach, ∼5%; EmPower, ∼7%; and Weba, ∼2%). Most differences from the Reference System in stroke-to-stroke variability of power were possibly or likely trivial or small for Peach (−3.0 to −16%), and likely or decisively substantial for EmPower (9.7–57%), and mostly decisively substantial for Weba (61–139%) and the Concept2 (−28 to 177%).Conclusion: Potential negative error in mean power was evident for all devices and units, particularly EmPower. Stroke-to-stroke variation in power showed a lack of measurement sensitivity (apparent smoothing) that was minor for Peach but larger for the Concept2, whereas EmPower and Weba added random error. Peach is therefore recommended for measurement of mean and stroke power.

The extent to which travel has affected Super Rugby teams' performances was analysed using outcomes of all matches played from the beginning of the competition in 1996 to the end of the 2016 season. Points difference and matches won or lost were predicted with general and generalized mixed linear models. The predictors were the linear effects of number of time zones crossed and travel duration based on the teams' locations for each match and their locations in the previous week. The away-match disadvantage was also estimated, along with trends in all these effects. In 1996 the predicted combined effect of eastward travel across 12 time zones was a reduction of 5.8 points scored per match, resulting in 4.1 more matches lost every 10 matches. Corresponding effects for westward travel were 6.4 points and 3.1 matches. In 2016 effects travelling eastward were 3.7 points and 2.3 matches, whereas travelling westward the effects were 3.7 points and 1.5 matches. These travel effects were due mainly to the away-match disadvantage: 5.7 points and 3.2 matches in 1996; 5.2 points and 2.3 matches in 2016. Teams in Super Rugby are dealing successfully with long-haul travel and should now focus on reducing the away-match disadvantage.

Background Wearable tracking devices are commonly utilised to quantify the external acceleration load of team sport athletes during training and competition. The ability to accelerate is an important attribute for athletes in many team sports. However, there are many different acceleration metrics that exist in team sport research. This review aimed to provide researchers and practitioners with a clear reporting framework on acceleration variables by outlining the different metrics and calculation processes that have been adopted to quantify acceleration loads in team sport research. Methods A systematic review of three electronic databases (CINAHL, MEDLINE, SPORTDiscus), was performed to identify peer-reviewed studies that published external acceleration load in elite team sports during training and/or competition. Articles published between January 2010 and April 2020 were identified using Boolean search phrases in relation to team sports (population), acceleration/deceleration (comparators), and competition and/or training (outcome). The included studies were required to present external acceleration and/or deceleration load (of any magnitude) from able-bodied athletes (mean age ≥ 18 years) via wearable technologies. Results A total of 124 research articles qualified for inclusion. In total, 113/124 studies utilised GPS/GNSS technology to outline the external acceleration load of athletes. Count-based metrics of acceleration were predominant of all metrics in this review (72%). There was a lack of information surrounding the calculation process of acceleration with 13% of studies specifying the filter used in the processing of athlete data, whilst 32% outlined the minimum effort duration (MED). Markers of GPS/GNSS data quality, including horizontal dilution of precision (HDOP) and the average number of satellites connected, were outlined in 24% and 27% of studies respectively. Conclusions Team sport research has predominantly quantified external acceleration load in training and competition with count-based metrics. Despite the influence of data filtering processes and MEDs upon acceleration, this information is largely omitted from team sport research. Future research that outlines acceleration load should present filtering processes, MEDs, HDOP, and the number of connected satellites. For GPS/GNSS systems, satellite planning tools should document evidence of available satellites for data collection to analyse tracking device performance. The development of a consistent acceleration filtering method should be established to promote consistency in the research of external athlete acceleration loads.

This pilot study examined the effect of inspiratory muscle training (IMT) on repeated-sprint ability and vastus lateralis reoxygenation. Ten recreationally trained subjects were randomly divided into two groups to complete 4 weeks of IMT or Sham (placebo) training. Pre- and post-intervention, a repeated-sprint ability (RSA) test was performed in both normoxia and hypoxia (FiO2 ≈ 14.5%). Vastus lateralis reoxygenation (VLreoxy), defined as peak to minimum amplitude deoxyhaemoglobin for each sprint/recovery cycle, was assessed during all trials using near-infrared spectroscopy. For total work performed, power analysis revealed that for small, medium and large effects (Cohen’s f), sample sizes of n = 8, 16 and 90 respectively, are required to achieve a power of 80% at an α level of 0.05. Maximal inspiratory mouth pressure increased in IMT by 36.5%, 95% CI [20.9, 61.6] and by 2.7%, 95% CI [−4.46, 8.8] in Sham. No clear difference in the change of work completed during the sprints between groups were observed in normoxia (Sham −0.805 kJ, 95% CI [−3.92, 0.39]; IMT −2.06 kJ, 95% CI [−11.5, 4.96]; P = 0.802), or hypoxia (Sham −3.09 kJ, 95% CI [−7, 0.396]; IMT 0.354 kJ, 95% CI [−1.49, 2.1]; P = 0.802). VLreoxy in IMT increased by 9.34%, 95% CI [5.15, 13.7] in normoxia only. In conclusion, despite a large increase in IMT, this was only associated with a small effect on RSA in our pilot study cohort. Owing to a potentially relevant impact of training the inspiratory musculature, future studies should include a sample size of at least 16-20 to detect moderate to large effects on RSA.

Purpose Boat acceleration profiles provide a valuable feedback tool by reflecting both rower technique and force application. Relationships between measures of boat acceleration and velocity to inform interpretation of boat acceleration profiles in rowing were investigated here. Methods Thirteen male singles, nine female singles, eight male pairs, and seven female pairs participated (national and international level, age 18–27 y). Data from each stroke for 74 2000-m races were collected using Peach PowerLine and OptimEye S5 GPS units. General linear mixed modelling established modifying effects on velocity of two within-crew SD of boat acceleration variables for each boat class, without and with adjustment for stroke rate and power, to identify potential performance-enhancement strategies for a given stroke rate and power. Measures of acceleration magnitude at six peaks or dips, and six measures of the rate of change (jerk) between these peaks and dips were analyzed. Results were interpreted using rejection of non-substantial and substantial hypotheses with a smallest substantial change in velocity of 0.3%. Results Several boat acceleration measures had decisively substantial effects (-2.4–2.5%) before adjustment for stroke rate and power. Most effect magnitudes reduced after adjustment for stroke rate and power, although maximum negative drive acceleration, peak drive acceleration, jerk during the mid-drive phase, and jerk in the late recovery remained decisively substantial (-1.8–1.9%) in some boat classes. Conclusion Greater absolute values of maximum negative drive acceleration and jerk in the late recovery are related to improved performance, likely reflecting delayed rower centre-of-mass negative acceleration in preparation for the catch. Greater absolute values of peak drive acceleration, first peak acceleration, and jerk in the early and mid-drive are also associated with improved performance, likely reflecting propulsive force during the drive. These proposed mechanisms provide potential strategies for performance enhancement additional to increases in stroke rate and power output.

Introduction: Accurate interpretation of activity profile data requires an understanding of the variables influencing player movement during matches. Methods: Over 65,000 stints (player rotations) from all 207 matches of the 2018 Australian Football League (AFL) season were evaluated. The relative activity profile including total distance per minute (TD), high-speed running distance per minute (HSR) and Player LoadTM per minute (PL) was determined for each stint and analysed against a range of match-related, player-related and environment-related predictor variables using multivariate linear mixed modelling. Effect size statistics along with the uncertainty in the estimates (95% confidence interval) were used to interpret the findings. Results: The smallest important effects on TD, HSR, and PL were calculated as 1.5%, 5.5%, and 2.4%, respectively. Stint duration had small to moderate negative effects on TD (−6%), PL (−7.7%), and HSR (−13%), while recovery duration between stints had a small positive effect on HSR (+7%). There were moderate reductions in TD (−8%), HSR (−23%), and PL (−9.6%) in the last quarter compared to the first quarter of matches, while similar reductions existed in subsequent stints compared to the first stint in each quarter. Moderate to large differences of up to 9% in TD, 48% in HSR and 12% in PL existed between positions. The TD of less experienced players was slightly higher than their more experienced counterparts (2–3%). A 5% increase in body mass was associated with a small reduction in HSR (−5.5%). There were small reductions in TD (−2%), HSR (−10%), and PL (−3%) during the Finals Series compared to the Premiership Season. Moderate levels of rainfall during matches and higher apparent temperatures had small negative effects on TD (−2%) and HSR (−6 and −9%). The number of days break between matches, score margin, match outcome, ground hardness, ground size, and traveling for the current or the previous match had trivial effects on the activity profile. Conclusion: Player position and stage of the match (quarter) had the largest effects on match activity profile while stint duration, recovery duration, stint timing, professional experience, body mass, stage of the season, and weather conditions also had substantial effects.

In healthy individuals at rest and while performing moderate-intensity exercise, systemic blood flow is distributed to tissues relative to their metabolic oxygen demands. During sustained high-intensity exercise, competition for oxygen delivery arises between locomotor and respiratory muscles, and the heightened metabolic work of breathing, therefore, contributes to limited skeletal muscle oxygenation and contractility. Intriguingly, this does not appear to be the case for intermittent-sprint work. This chapter presents new evidence, based on inspiratory muscle mechanical loading and hypoxic gas breathing, to support that the respiratory system of healthy men is capable of accommodating the oxygen needs of both locomotor and respiratory muscles when work is interspersed with short recovery periods. Only when moderate hypoxemia is induced, substantial oxygen competition arises in favour of the respiratory muscles. These findings extend our understanding of the relationship between mechanical and metabolic limits of varied exercise modes.

Graham, S, Duthie, G, Aughey, R, and Zois, J. Comparison of physical profiles of state-level netball players by position. J Strength Cond Res 34(9): 2654-2662, 2020-Understanding the physical profiles of state-level netball athletes provides conditioning professionals with testing norms and an understanding of important capacities within particular positions. The purpose was to examine positional differences in physical capacities of state-level netball athletes. Forty-six state-level netball athletes completed physical capacity assessments in the second week of their preseason, over 2 seasons. Tests included stature, 20-m sprint (with 5 and 10 m splits), 505 change-of-direction, countermovement jump, single-leg bounding, and the Yo-Yo intermittent recovery test level one. A linear mixed-model with fixed and random effects was used in conjunction with magnitude-based inferences to determine meaningful differences with 90% confidence limits (CLs). Midcourt athletes were shorter in stature than shooters (effect size [ES]: 1.26, CL 0.88-1.63) and defenders (-2.58, -3.35 to -1.80). Midcourt athletes were faster than shooters and defenders in linear acceleration over 5 and 10 m distances and 505 change-of-direction (ES range: -0.34 to -0.59). Shooters presented poorer intermittent endurance than midcourt (-0.82, -1.34 to -0.31) and defenders (-0.56, -1.19 to 0.07). Defenders jumped higher than midcourt athletes (-0.56, -1.30 to 0.19) and shooters (-0.61, -1.18 to -0.04), and possessed greater bounding ability compared with shooters (ES range: -0.23 to -0.57). Midcourt athletes require a more holistic set of physical capacities including acceleration, change-of-direction, jumping, and intermittent endurance. For defender and shooter positions, results indicate physical requirements that may be more specific to their positions. Conditioning professionals can use these findings to target specific physical qualities to maximize position-specific performance.

Near-infrared spectroscopy (NIRS) is a common tool used to study oxygen availability and utilization during repeated-sprint exercise. However, there are inconsistent methods of smoothing and determining peaks and nadirs from the NIRS signal, which make interpretation and comparisons between studies difficult. To examine the effects of averaging method on deoxyhaemoglobin concentration ([HHb]) trends, nine males performed ten 10-s sprints, with 30 seconds of recovery, and six analysis methods were used for determining peaks and nadirs in the [HHb] signal. First, means were calculated over predetermined windows in the last 5 and 2 seconds of each sprint and recovery period. Second, moving 5-seconds and 2-seconds averages were also applied, and peaks/nadirs were determined for each 40-seconds sprint/recovery cycle. Third, a Butterworth filter was used to smooth the signal, and the resulting signal output was used to determine peaks and nadirs from predetermined time points and a rolling approach. Correlation and residual analysis showed that the Butterworth filter attenuated the "noise" in the signal, while maintaining the integrity of the raw data (r = .9892; mean standardized residual -9.71 × 103 ± 3.80). Means derived from predetermined windows, irrespective of length and data smoothing, underestimated the magnitude of peak and nadir [HHb] compared to a rolling mean approach. Consequently, sprint-induced metabolic changes (inferred from Δ[HHb]) were underestimated. Based on these results, we suggest using a digital filter to smooth NIRS data, rather than an arithmetic mean, and a rolling approach to determine peaks and nadirs for accurate interpretation of muscle oxygenation trends.

Effects of fixture and team characteristics on match outcome in elite Australian football were quantified using data accessed at AFLtables.com for 5109 matches for seasons 2000 to 2013. Aspects of each match included number of days' break between matches (≤7 d vs ≥8 d), location (home vs away), travel status (travel vs no travel), and differences between opposing teams' mean age, body mass, and height (expressed as quintiles). A logistic-regression version of the generalized mixed linear model estimated each effect, which was assessed with magnitude-based inference using 1 extra win or loss in every 10 matches as the smallest important change. For every 10 matches played, the effects were days' break, 0.1 ± 0.3 (90% CL) wins; playing away, 1.5 ± 0.6 losses; traveling, 0.7 ± 0.6 losses; and being in the oldest, heaviest, or shortest, quintile, 1.9 ± 0.4, 1.3 ± 0.4, and 0.4 ± 0.4 wins, respectively. The effects of age and body-mass difference were not reduced substantially when adjusted for each other. All effects were clear, mostly at the 99% level. The effects of playing away, travel, and age difference were not unexpected, but the trivial effect of days' break and the advantage of a heavier team will challenge current notions about balancing training with recovery and about team selection.

Knowledge of the most intense periods of competitive soccer may assist in the development of specific training methodologies. Objectives: To quantify the peak running intensity of professional soccer and to establish the rate of decline in this intensity as a function of time. Methods: Activity profiles were obtained from 24 players across 40 professional matches. Peak values were calculated for moving averages 1–10 minutes in duration for relative distance (m∙min−1), high-speed relative distance (HS m∙min−1), average acceleration/deceleration (m∙s2) and metabolic power (Pmet). To quantify the decrease in running intensity for longer moving average durations, each measure was evaluated relative to the moving average duration, as a power law relationship. Results: Peak relative distance and Pmet were lowest for central defenders (effect size [ES] = 0.79–1.84), whilst acceleration/deceleration intensity was highest for wide defenders (ES = 0.67–1.42). Differences in the rate of decline in running intensity between positions were considered trivial to small, indicating a similar rate of decline in running intensity across positions. Conclusions: Using power law, the peak running intensities of professional soccer can now be predicted as a function of time, providing coaches with a useful tool for the prescription and monitoring of specific training drills.

Purpose:Detrimental changes in tendon structure increase the risk of tendinopathies. The aim of this study was to investigate the influence of individual internal and external training loads and leg dominance on changes in the Achilles and patellar tendon structure. Methods:The internal structure of the Achilles and patellar tendons of both limbs of 26 elite Australian footballers was assessed using ultrasound tissue characterization at the beginning and the end of an 18-wk preseason. Linear-regression analysis was used to estimate the effects of training load on changes in the proportion of aligned and intact tendon bundles for each side. Standardization and magnitude-based inferences were used to interpret the findings. Results: Possibly to very likely small increases in the proportion of aligned and intact tendon bundles occurred in the dominant Achilles (initial value 81.1%; change, ±90% confidence limits 1.6%, ±1.0%), nondominant Achilles (80.8%; 0.9%, ±1.0%), dominant patellar (75.8%; 1.5%, ±1.5%), and nondominant patellar (76.8%; 2.7%, ±1.4%) tendons. Measures of training load had inconsistent effects on changes in tendon structure; eg, there were possibly to likely small positive effects on the structure of the nondominant Achilles tendon, likely small negative effects on the dominant Achilles tendon, and predominantly no clear effects on the patellar tendons. Conclusion:The small and inconsistent effects of training load are indicative of the role of recovery between tendon-overloading (training) sessions and the multivariate nature of the tendon response to load, with leg dominance a possible influencing factor.

Objectives To determine the efficacy of live-high train-low on team-sport athlete physical capacity and the time-course for adaptation. Design Pre-post parallel-groups. Methods Fifteen Australian footballers were matched for Yo-Yo Intermittent recovery test level 2 (Yo-YoIR2) performance and assigned to LHTL ( n =7) or control (Con; n =8). LHTL spent 19 nights (3×5 nights, 1×4 nights, each block separated by 2 nights at sea level) at 3000-m simulated altitude (F I O 2 : 0.142). Yo-Yo IR2 was performed pre and post 5, 15, and 19 nights. A 2- and 1-km time-trial (TT) was performed pre and post intervention. Haemoglobin mass (Hb mass ) was measured in LHTL after 5, 10, 15, and 19 nights. A contemporary statistical approach using effect size, confidence limits, and magnitude-based inferences was used to measure changes between groups. Results Compared to pre, Hb mass was possibly higher after 15 (3.8%, effect size (ES) 0.19, 90% confidence limits 0.05–0.33) and very likely higher after 19 nights (6.7%, 0.35, 0.10; 0.52). For Yo-Yo IR2, LHTL group change was not meaningfully different to Con after 5 nights, possibly greater after 15 (10.2%, 0.37, −0.29; 1.04), and likely greater after 19 nights (13.5%, 0.49, −0.16; 1.14). Both groups improved 2-km TT, with LHTL improvement possibly higher than CON (1.9%, 0.22, −0.18; 0.62). Only LHTL improved 1-km TT, with LHTL improvement likely greater than CON (4.6%, 0.56, −0.08; 1.04). Conclusions Fifteen nights of LHTL was possibly effective, while 19 nights was effective at increasing Hb mass , Yo-Yo IR2 and repeated TT performance more than sea-level training.

A high work of breathing can compromise limb oxygen delivery during sustained high-intensity exercise. However, it is unclear if the same is true for intermittent sprint exercise. This project examined the addition of an inspiratory load on locomotor muscle tissue reoxygenation during repeated-sprint exercise. Ten healthy males completed three experimental sessions of ten 10 s sprints, separated by 30 s of passive rest on a cycle ergometer. The first two sessions were "all-out efforts performed without (CTRL) or with inspiratory loading (INSP) in a randomised and counterbalanced order. The third experimental session (MATCH) consisted of ten 10 s work-matched intervals. Tissue saturation index (TSI) and deoxy-haemoglobin (HHb) of the vastus lateralis and sixth intercostal space was monitored with near-infrared spectroscopy. Vastus lateralis reoxygenation ({Delta}Reoxy) was calculated as the difference from peak HHb (sprint) to nadir HHb (recovery). Total mechanical work completed was similar between INSP and CTRL (effect size: -0.18, 90% confidence limit {+/-}0.43), and differences in vastus lateralis TSI during the sprint (-0.01, {+/-}0.33) and recovery (-0.08, {+/-}0.50) phases were unclear. There was also no meaningful difference in {Delta}Reoxy (0.21, {+/-}0.37). Intercostal HHb was higher in the INSP session compared to CTRL (0.42, {+/-}0.34), whilst the difference was unclear for TSI (-0.01, {+/-}0.33). During MATCH exercise, differences in vastus lateralis TSI were unclear compared to INSP for both sprint (0.10, {+/-}0.30) and recovery (-0.09, {+/-}0.48) phases, and there was no meaningful difference in {Delta}Reoxy (-0.25, {+/-}0.55). Intercostal TSI was higher during MATCH compared to INSP (0.95, {+/-}0.53), whereas HHb was lower (-1.09, {+/-}0.33). The lack of difference in {Delta}Reoxy between INSP and CTRL suggests that for intermittent sprint exercise, the metabolic O2 demands of both the respiratory and locomotor muscles can be met. Additionally, the similarity of the MATCH suggests that {Delta}Reoxy was maximal in all exercise conditions.

Objectives To investigate the peak accelerometer-derived intensity of state-level netball matches and compare differences between positional groupings. Findings will provide guidance for sport science professionals on how to best replicate the most intense passages of play in training settings. Design Longitudinal (one season). Method Twenty-eight netball athletes across three teams from the same club wore an accelerometer (S5 Optimeye, Catapult sports) for all matches, in one season. Raw acceleration data were downloaded and converted into a vector magnitude (Player Load™) we then quantified the peak intensity over 30-s and one to ten-minute time periods. Positional groupings were created based on the number of thirds on a netball court that a particular position can enter, as this was deemed more appropriate for the current study than the traditional combinations based on tactical requirements. A linear mixed-model with fixed and random effects was utilised along with magnitude-based inferences to determine meaningful differences with 90 % confidence limits (CL). Results Across all time periods post 30-s, only one comparison was not meaningfully different i.e. three-thirds v two-thirds at the one-minute timepoint (effect size: 0.27, CL −0.05 to 0.60). Conclusions Findings justify that netball athletes, depending on positional group defined by this study, should train at different intensities dependent on a specified duration. Conditioning professionals and coaches should design training drills that best replicate the peak intensity of match play. This may improve an athlete’s physical performance capacity during highly exertive periods of competition, which regularly occur at critical moments in play.

Objectives To compare recent internal training load and strain of elite Australian football players for match outcome. Design Case study. Methods Load was quantified from session rating of perceived exertion (sRPE) for individual players from one team in 141 professional Australian football matches over six seasons, then averaged for players that competed for the team each week. Internal weekly-load and weekly-strain (load × monotony) was compared to recent-load and recent-strain (four-week rolling average) as a marker of training-stress balance for each player against the match outcome. Covariates for relative position of teams in the competition and days between matches were modelled. Differences were standardised (effect size; ES) and interpreted using magnitude based inferences. Results Weekly-load was likely higher for match wins (ES ± 90% confidence limits; 0.43 ± 0.27), and when days-break was used as a covariate (0.45 ± 0.27) but only possibly higher with relative ladder position covaried (RLP, 0.29 ± 0.33). There was a possibly greater positive training–stress balance for load in wins (0.31; ±0.38) with db (0.39; ±0.39) and RLP covaried (0.27; ±0.48). There were no clear differences for strain for wins and losses or with either covariate. There was a likely greater positive training-stress balance for strain in wins (0.51; ±0.41) with days-break (0.48; ±0.41) but not RLP covaried. Conclusions Weekly-load and a positive training-stress balance for strain were the best predictors of match success. The higher weekly-load and training-stress balance for strain highlight the conflict between maintaining the training stimulus and minimising fatigue in Australian football players between matches.

Purpose:To determine the time course for physical-capacity adaptations to intermittent hypoxic training (IHT) in team-sport athletes and the time course for benefits remaining after IHT.Methods:A pre–post parallel-groups design was employed, with 21 Australian footballers assigned to IHT (n = 10) or control (CON; n = 11) matched for training load. IHT performed eleven 40-min bike sessions at 2500-m altitude over 4 wk. Yo-Yo Intermittent Recovery Test level 2 (Yo-Yo IR2) was performed before; after 3, 6, and 11 IHT sessions; and 30 and 44 d after IHT. Repeated time trials (2- and 1-km TTs, with 5 min rest) were performed before, after, and 3 wk after IHT. Hemoglobin mass (Hbmass) was measured in IHT before and after 3, 6, 9, and 11 sessions.Results:Baseline Yo-Yo IR2 was similar between groups. After 6 sessions, the change in Yo-Yo IR2 in IHT was very likely higher than CON (27% greater change, effect size 0.77, 90% confidence limits 0.20;1.33) and likely higher 1 d after IHT (23%, 0.68, 0.05;1.30). The IHT group’s change remained likely higher than CON 30 d after IHT (24%, 0.72, 0.12;1.33) but was not meaningfully different 44 d after (12%, 0.36, –0.24;0.97). The change in 2-km TT performance between groups was not different throughout. For 1-km TT, CON improved more after IHT, but IHT maintained performance better after 3 wk. Hbmass was higher after IHT (2.7%, 0.40, –0.40;1.19).Conclusion:Short-duration IHT increased Yo-Yo IR2 compared with training-load-matched controls in 2 wk. An additional 2 wk of IHT provided no further benefit. These changes remained until at least 30 d posttraining. IHT also protected improvement in 1-km TT.

Training prescription and monitoring of team-sport athletes rely on accurate quantification of player movement. Our aim was to determine the sensitivity, reliability and construct validity of measures derived from a wearable device incorporating Global Positioning System (GPS) and accelerometer technology to quantify the peak periods of rugby competition. Match movement data were collected from 30 elite and 30 sub-elite rugby union players across respective competitive seasons. Accelerometer and GPS measures were analysed using a rolling average to identify peak movement for epochs ranging from 5 to 600 seconds. General linear mixed modelling was used to quantify the effects of playing position and match-half on the peak movement and variabilities within and between players represented reliability of each measure. Mean positional differences and match-half changes were assessed via standardisation and magnitude-based decisions. Sensitivity of measures was quantified via evaluation of ("signal") and typical error of measurement ("noise"). GPS and accelerometer measures had poor sensitivity for quantifying peak movement across all epochs and both levels of rugby union competition (noise 4× to 5× the signal). All measures displayed correspondingly low reliability across most epochs and both levels of competition (ICC

This study aimed to investigate whether exercise hyperpnoea contributes to an impairment of locomotor muscle oxygenation and performance during repeated-sprint exercise in normoxia and hypoxia. Subjects performed ten 10-s sprints, separated by 30 s of passive rest while breathing either a normoxic (21% O2) or hypoxic (15% O2) gas mixture. Muscle oxygenation of the vastus lateralis and intercostal muscles was examined with near-infrared spectroscopy. Sprint and recovery vastus lateralis deoxyhaemoglobin was elevated in hypoxia by 9.2% (90% confidence interval 0.2 to 18.0) and 14.1% (90% CL 4.9 to 23.3%) compared to normoxia, respectively. There were no clear differences in respiratory muscle deoxyhaemoglobin (−0.1%, 90% CL −2.9 to 0.9%) or oxyhaemoglobin (0.9%, 90% CL −0.8 to 2.6%) between conditions. Maintenance of respiratory muscle oxygenation may contribute to the rise of vastus lateralis deoxyhaemoglobin in hypoxia during intermittent sprint cycling. This manuscript presents data which extends the fact that oxygen competition could be a limiting factor of exercise capacity.

Aim: Relationships between athlete monitoring-derived variables and injury risk have been investigated predominantly in isolation. The aim of this study was to evaluate the individual and combined effects of multiple factors on the risk of soft-tissue non-contact injuries in elite team sport athletes. Methods: Fifty-five elite Australian footballers were prospectively monitored over two consecutive seasons. Internal and external training load was quantified using the session rating of perceived exertion and GPS/accelerometry, respectively. Cumulative load and acute-to-chronic workload ratios were derived using rolling averages and exponentially weighted moving averages. History of injuries in the current and previous seasons was recorded along with professional experience, weekly musculoskeletal screening, and subjective wellness scores for individual athletes. Individual and combined effects of these variables on injury risk were evaluated with generalized linear mixed models. Results: High cumulative loads and acute-to-chronic workload ratios were associated with increased risk of injuries. The effects for measures derived using exponentially weighted moving averages were greater than those for rolling averages. History of a recent injury, long-term experience at professional level, and substantial reductions in a selection of musculoskeletal screening and subjective wellness scores were associated with increased risk. The effects of high cumulative loads were underestimated by ~20% before adjusting for previous injuries, whereas the effects of high acute-to-chronic workload ratios were overestimated by 10-15%. Injury-prone players, identified via player identity in the mixed model, were at > 5 times higher risk of injuries compared to robust players (hazard ratio 5.4, 90% confidence limits 3.6-12) despite adjusting for training load and previous injuries. Combinations of multiple risk factors were associated with extremely large increases in risk; for example, a hazard ratio of 22 (9.7-52) was observed for the combination of high acute load, recent history of a leg injury, and a substantial reduction in the adductor squeeze test score. Conclusion: On the basis of our findings with an elite team of Australian footballers, the information from athlete monitoring practices in team sports should be interpreted collectively and used as a part of the injury prevention decision-making process along with consideration of individual differences in risk.

To compare relative match intensities of sea-level versus high-altitude native soccer players during a 2-week camp at 3600 m, data from 7 sea-level (Australian U17 National team, AUS) and 6 high-altitude (a Bolivian U18 team, BOL) native soccer players were analysed. Two matches were played at sea-level and three at 3600 m on Days 1, 6 and 13. The Yo-Yo Intermittent recovery test (vYo-YoIR1) was performed at sea-level, and on Days 3 and 10. Match activity profiles were measured via 10-Hz GPS. Distance covered >14.4 km.h-1 (D>14.4 km·h-1) and >80% of vYo-YoIR1 (D>80%vYo-YoIR1) were examined. Upon arrival at altitude, there was a greater decrement in vYo-YoIR1 (Cohen’s d +1.0, 90%CL ± 0.8) and D>14.4 km·h-1 (+0.5 ± 0.8) in AUS. D>14.4 km.h-1 was similarly reduced relative to vYo-YoIR1 in both groups, so that D>80%vYo-YoIR1 remained similarly unchanged (-0.1 ± 0.8). Throughout the altitude sojourn, vYo-YoIR1 and D>14.4 km·h-1 increased in parallel in AUS, so that D>80%vYo-YoIR1 remained stable in AUS (+6.0%/match, 90%CL ± 6.7); conversely D>80%vYo-YoIR1 decreased largely in BOL (-12.2%/match ± 6.2). In sea-level natives competing at high-altitude, changes in match running performance likely follow those in high-intensity running performance. Bolivian data confirm that increases in ‘fitness’ do not necessarily translate into greater match running performance, but rather in reduced relative exercise intensity.

The punt kick is a fundamental skill used in several team sports; however, there has been a lack of research on how fatigue affects its technique. The purpose of this study was to determine the effects of short-term fatigue on punt-kicking performance. Eight elite and sub-elite Australian Football players performed maximal drop punt kicks on their preferred leg prior to, during and after a match-specific fatigue protocol. Optotrak Certus collected kinematic data from kick foot toe-off until ball contact. Repeated-measures analysis of variance showed a significant increase in 20 m sprint times after each short-term protocol, indicating fatigue. Foot speed did not significantly change with fatigue; however, increases in the range of motion at the pelvis and kicking thigh, along with increases in kicking thigh angular velocity, occurred. For the support leg, maximum knee flexion angular velocity increased while there was greater flexion found at the knee and hip, and greater range of motion at the knee. Players are able to make kinematic adaptations in order to maintain foot speed while punting for maximal distance after short-term efforts.

The validity of an Ultra-wideband (UWB) positioning system was investigated during linear and change-of-direction (COD) running drills. Six recreationally-active men performed ten repetitions of four activities (walking, jogging, maximal acceleration, and 45º COD) on an indoor court. Activities were repeated twice, in the centre of the court and on the side. Participants wore a receiver tag (Clearsky T6, Catapult Sports) and two reflective markers placed on the tag to allow for comparisons with the criterion system (Vicon). Distance, mean and peak velocity, acceleration, and deceleration were assessed. Validity was assessed via percentage least-square means difference (Clearsky-Vicon) with 90% confidence interval and magnitude-based inference; typical error was expressed as within-subject standard deviation. The mean differences for distance, mean/peak speed, and mean/peak accelerations in the linear drills were in the range of 0.2–12%, with typical errors between 1.2 and 9.3%. Mean and peak deceleration had larger differences and errors between systems. In the COD drill, moderate-to-large differences were detected for the activity performed in the centre of the court, increasing to large/very large on the side. When filtered and smoothed following a similar process, the UWB-based positioning system had acceptable validity, compared to Vicon, to assess movements representative of indoor sports.

Athlete external load is typically analysed from predetermined movement thresholds. The combination of movement sequences and differences in these movements between playing positions is also currently unknown. This study developed a method to discover the frequently recurring movement sequences across playing position during matches. The external load of 12 international female netball athletes was collected by a local positioning system during four national-level matches. Velocity, acceleration and angular velocity were calculated from positional (X, Y) data, clustered via one-dimensional k-means and assigned a unique alphabetic label. Combinations of velocity, acceleration and angular velocity movement were compared using the Levenshtein distance and similarities computed by the longest common substring problem. The contribution of each movement sequence, according to playing position and relative to the wider data set, was then calculated via the Minkowski distance. A total of 10 frequently recurring combinations of movement were discovered, regardless of playing position. Only the wing attack, goal attack and goal defence playing positions are closely related. We developed a technique to discover the movement sequences, according to playing position, performed by elite netballers. This methodology can be extended to discover the frequently recurring movements within other team sports and across levels of competition.

In this study accelerometers were more effective at quantifying important differences in movement between player positions and match halves compared to global positioning systems (GPS) during matches in a professional collision-based team sport, rugby union. The purpose of the present investigation was to determine the effectiveness of the respective technologies for detecting differences in measures of maximum mean movement between positions and halves during professional rugby union match-play. METHODS: Movement data were collected via integrated GPS and accelerometer units (OptimEye S5, Catapult Sports, Melbourne, Australia) for 30 professional rugby union players (16 forwards, 14 backs) across an eight-match season (256 match-half files). GPS sampling frequency was 10 Hz, while the accelerometer sampled at 100 Hz with an output range of ± 16 g. Accelerometer-derived PlayerLoad™ and GPS-derived measures of mean speed and metabolic power were analysed using a rolling average method to identify the maximum mean values across a 600-s epoch. General linear mixed modelling was used to predict measures, with fixed effects for player positions (backs, forwards) and match half (1st, 2nd), and with random effects for within-player variabilities, between-player differences, and between-match differences. Mean differences (backs - forwards) and changes (1st – 2nd) were assessed via standardization and magnitude based inference. RESULTS: Accelerometer derived PlayerLoad™ displayed moderate to large clear positional differences by half (standardized difference, ±90% confidence limits: 1st half –1.50, ±1.03; 2nd half −0.75; ±0.71) compared to generally unclear differences as quantified by GPS. All measures displayed clear half differences; however, PlayerLoad™ quantified the greatest magnitude of decline between-halves (forwards 1.66, ±0.71; backs 0.90, ±0.48). CONCLUSIONS: Positional and half differences in player maximum mean movement showed that GPS technology alone underestimated the activity of athletes during professional rugby union match-play. Accelerometers provide additional meaningful information that may aid practitioners in physically preparing and monitoring athletes in collision-based team sports.

Objective measures of recovery from football match play could be useful for assessing athletes’ readiness to train, if sensitive to preceding match load.Purpose:To identify the sensitivity of countermovement-jump (CMJ) performance and concentration of salivary testosterone and cortisol relative to elite football match load.Methods:CMJ performance and salivary hormones were measured in 18 elite football players before (27, 1 h) and after (0.5, 18, 42, 66, 90 h) 3 consecutive matches. Match load was determined via accelerometer-derived PlayerLoad and divided into tertiles. Sensitivity of CMJ performance and hormone concentrations to match load was quantified with t statistics and magnitude-based inferences (change in mean as % ± 90% confidence interval) derived with a linear mixed model.Results:Jump height was reduced in medium and high load at 0.5 h (10% ± 7% and 16% ± 8%) and 18 h (7% ± 4% and 9% ± 5%) postmatch. There was a 12% ± 7% reduction in ratio of flight time to contraction time (FT:CT) in high load at 0.5 h post, with reductions in medium and high load at 18 h. Reductions in FT:CT persisted at later postmatch time points than changes in jump height. Increased cortisol (range 55–165%) and testosterone (range 17–20%) were observed in all match loads at 0.5 h post, with individual variability thereafter.Conclusions:Measures of CMJ performance and hormonal concentrations were sensitive to levels of A League football match load. Although jump height was reduced immediately postmatch, FT:CT provided a more sensitive measure of recovery. Football match play induces an acute hormonal response with substantial individual variability thereafter.

Purpose: To quantify the effect of acute hypoxia on muscle oxygenation and power during simulated team-sport running. Methods: Seven individuals performed repeated and single sprint efforts, embedded in a simulated team-sport running protocol, on a non-motorized treadmill in normoxia (sea-level), and acute normobaric hypoxia (simulated altitudes of 2,000 and 3,000 m). Mean and peak power was quantified during all sprints and repeated sprints. Mean total work, heart rate, blood oxygen saturation, and quadriceps muscle deoxyhaemoglobin concentration (assessed via near-infrared spectroscopy) were measured over the entire protocol. A linear mixed model was used to estimate performance and physiological effects across each half of the protocol. Changes were expressed in standardized units for assessment of magnitude. Uncertainty in the changes was expressed as a 90% confidence interval and interpreted via non-clinical magnitude-based inference. Results: Mean total work was reduced at 2,000 m (−10%, 90% confidence limits ±6%) and 3,000 m (−15%, ±5%) compared with sea-level. Mean heart rate was reduced at 3,000 m compared with 2,000 m (−3, ±3 min−1) and sea-level (−3, ±3 min−1). Blood oxygen saturation was lower at 2,000 m (−8, ±3%) and 3,000 m (−15, ±2%) compared with sea-level. Sprint mean power across the entire protocol was reduced at 3,000 m compared with 2,000 m (−12%, ±3%) and sea-level (−14%, ±4%). In the second half of the protocol, sprint mean power was reduced at 3,000 m compared to 2,000 m (−6%, ±4%). Sprint mean peak power across the entire protocol was lowered at 2,000 m (−10%, ±6%) and 3,000 m (−16%, ±6%) compared with sea-level. During repeated sprints, mean peak power was lower at 2,000 m (−8%, ±7%) and 3,000 m (−8%, ±7%) compared with sea-level. In the second half of the protocol, repeated sprint mean power was reduced at 3,000 m compared to 2,000 m (−7%, ±5%) and sea-level (−9%, ±5%). Quadriceps muscle deoxyhaemoglobin concentration was lowered at 3,000 m compared to 2,000 m (−10, ±12%) and sea-level (−11, ±12%). Conclusions: Simulated team-sport running is impaired at 3,000 m compared to 2,000 m and sea-level, likely due to a higher muscle deoxygenation.

The Na+-K+-ATPase (NKA) plays a key role in muscle excitability, but little is known in human skeletal muscle about fiber-type-specific differences in NKA isoform expression or adaptability. A vastus lateralis muscle biopsy was taken in 17 healthy young adults to contrast NKA isoform protein relative abundance between type I and IIa fibers. We further investigated muscle fiber-type-specific NKA adaptability in eight of these adults following 4-wk repeated-sprint exercise (RSE) training, comprising three sets of 5 × 4-s sprints, 3 days/wk. Single fibers were separated, and myosin heavy chain (I and IIa) and NKA (α1–3and β1–3) isoform abundance were determined via Western blotting. All six NKA isoforms were expressed in both type I and IIa fibers. No differences between fiber types were found for α1-, α2-, α3-, β1-, or β3-isoform abundances. The NKA β2-isoform was 27% more abundant in type IIa than type I fibers ( P < 0.05), with no other fiber-type-specific trends evident. RSE training increased β1in type IIa fibers (pretraining 0.70 ± 0.25, posttraining 0.84 ± 0.24 arbitrary units, 42%, P < 0.05). No training effects were found for other NKA isoforms. Thus human skeletal muscle expresses all six NKA isoforms and not in a fiber-type-specific manner; this points to their different functional roles in skeletal muscle cells. Detection of elevated NKA β1after RSE training demonstrates the sensitivity of the single-fiber Western blotting technique for fiber-type-specific intervention effects.