The influence of external loads on post-match neuromuscular fatigue in international rugby union: A partial least squares correlational analysis.

The aims were to determine the relationship(s) between match-play external load and post-match neuromuscular fatigue as latent constructs, the contribution of the specific measured variables to these latent constructs, and how these differ between forwards and backs in elite rugby union. Forty-one elite male rugby union players (22 forwards and 19 backs) from the same international rugby union team were tested, with data included from the 2020 and 2021 international seasons (11 matches; 146 player appearances). Player's match-play external loads were quantified using microtechnology (for locomotor activities) and video analysis (for collision actions). Neuromuscular fatigue was quantified using countermovement jump tests on force plates which were conducted ~ 24 to 48 hours pre- and post-match. Partial least squares correlation (PLSC) leave one variable out (LOVO) procedure established the relative variable contribution to both external load (X matrix) and neuromuscular fatigue (Y matrix) constructs. Linear mixed-effects models were then constructed to determine the variance explained by the latent scores applied to the variables representing these constructs. For external load, both locomotor and collision variables were identified for the forwards and the backs, although the identified variables differed between groups. For neuromuscular fatigue, jump height was identified as a high contributor for the forwards and the backs, with concentric impulse and reactive strength index high contributors only for the backs. The explained variance between the external load and neuromuscular fatigue latent constructs at the individual player level was 4.4% and 32.2% in the forwards and the backs models, respectively. This discrepancy may be explained by differences in match-play external loads and/or the specificity of the tests to measure indicators of fatigue. These may differ due to, for example, the activities undertaken in the different positional groups.

The impact of internal and external loads on player performance in Chinese basketball association.

BACKGROUND: Limited research has investigated the association between training load and performance of basketball players during games. Little is known about how different indicators of player performance are affected by internal and external loads. The purpose of this study was to determine whether external and internal loads influence basketball players' performance during games. METHOD: This longitudinal study involved 20 professional male basketball players from a single team, classified as first-level athletes by the Chinese Basketball Association. During 34 games, external load was measured as PlayerLoad using micro-sensors, while internal load was assessed using session rating of perceived exertion (sRPE). Player performance was quantified using three metrics: Efficiency, Player Index Rating (PIR), and Plus-Minus (PM). Pearson correlation coefficients were calculated to assess the strength of the relationships between training loads and performance metrics. Linear mixed-effects models were applied to further analyze the influence of internal and external loads on basketball performance. RESULTS: Pearson correlation analysis revealed moderate positive correlations between both sRPE and PlayerLoad with Efficiency and PIR. Specifically, sRPE (r = 0.52) and PlayerLoad (r = 0.54) were both significantly correlated with Efficiency. For PIR, sRPE (r = 0.50) and PlayerLoad (r = 0.56) also demonstrated moderate correlations. These correlations were further substantiated by linear mixed-effects models, which showed that sRPE (ß = 2.21, p < 0.001) and PlayerLoad (ß = 1.87, p = 0.004) had significant independent effects on Efficiency. Similarly, sRPE (ß = 2.15, p < 0.001) and PlayerLoad (ß = 2.36, p < 0.001) significantly predicted PIR. Additionally, a significant interaction effect between PlayerLoad and sRPE was found on Plus-Minus (ß = -2.49, p < 0.001), indicating that the combination of high physical and psychological loads negatively impacted overall team performance. However, the correlation strengths for Plus-Minus were relatively low (sRPE: r = 0.16; PlayerLoad: r = 0.10). CONCLUSION: Both external and internal loads positively contribute to performance, the integration of objective (accelerometry) and subjective (sRPE) measures of load provides a comprehensive understanding of the physiological and psychological demands on athletes, contributing to more effective training regimens and performance optimization.

Assessing pre-season workload variation in professional rugby union players by comparing three acute:Chronic workload ratio models based on playing positions.

Quantifying the pre-season workload of professional Rugby Union players, in relation to their respective positions not only provides crucial insights into their physical demands and training needs but also underscores the significance of the acute:chronic workload ratio (ACWR) in assessing workload. However, given the diversity in ACWR calculation methods, their applicability requires further exploration. As a result, this study aims to analyze the workload depending on the player's positions and to compare three ACWR calculation methods. Fifty-seven players were categorized into five groups based on their playing positions: tight five (T5), third-row (3R), number nine (N9), center, and third line defense (3L). The coupled and uncoupled rolling averages (RA), as well as the exponentially weighted moving average ACWR method, were employed to compute measures derived from GPS data. Changes throughout the pre-season were assessed using the one-way and two-way analysis of variance. The results revealed that N9 covered significantly greater distances and exhibited higher player load compared to T5 and 3L [p < 0.05, effect size (ES) = 0.16-0.68]. Additionally, 3L players displayed the highest workload across various measures, including counts of accelerations and decelerations (>2.5 m s-2), accelerations (>2.5 m s-2), acceleration distance (>2 m s-2), high-speed running (>15 km h-1), very high-speed running (>21 km h-1, VSHR), sprint running (>25 km h-1, SR) distance. When using coupled RA ACWR method, centers exposed significantly greater values to T5 (p < 0.05, ES = 0.8) and 3R (p < 0.05, ES = 0.83). Moreover, centers exhibited greater (p < 0.05, ES = 0.67-0.91) uncoupled RA ACWR values for VHSR and SR than T5 and 3R. When comparing the three ACWR methods, although significant differences emerged in some specific cases, the ES were all small (0-0.56). In light of these findings, training should be customized to the characteristics of players in different playing positions and the three ACWR calculation methods can be considered as equally effective approaches.

Training loads and microcycle periodisation in Italian Serie A youth soccer players.

Microcycles are fundamental structures for training prescription and load management, helping to optimise training effects and performance. This study quantified external and internal loads of Italian Serie A youth soccer players across competitive weeks and their periodisation within microcycles. Data were collected from 90 players belonging to four age groups (under-19, -17, -16, -15) across a season. Methods of monitoring external [duration and global navigation satellite systems (GNSS)] and internal load [heart rate (HR) and rating of perceived exertion (RPE)] were employed. Linear mixed models determined differences in training loads across age groups, training days and player positions. Under-19 and under-17 players trained five times per week, while younger players trained four times. Late-stage academy players (under-19 and -17) demonstrated higher weekly accumulated external and sRPE training load compared to their younger counterparts (p < 0.05 between groups). Weekly accumulated HR internal loads were higher in under-15 players (p < 0.05 between groups). Marked fluctuations of daily load were observed across microcycles in under-19 and under-17 groups (p < 0.05 between days). These findings highlight progressive increases in training load throughout the development pathway, with late-stage academy players training with higher frequency, volume and marked periodisation compared to younger players.

Kinematics-Based Predictions of External Loads during Handcycling.

The increased risk of cardiovascular disease in people with spinal cord injuries motivates work to identify exercise options that improve health outcomes without causing risk of musculoskeletal injury. Handcycling is an exercise mode that may be beneficial for wheelchair users, but further work is needed to establish appropriate guidelines and requires assessment of the external loads. The goal of this research was to predict the six-degree-of-freedom external loads during handcycling from data similar to those which can be measured from inertial measurement units (segment accelerations and velocities) using machine learning. Five neural network models and two ensemble models were compared against a statistical model. A temporal convolutional network (TCN) yielded the best predictions. Predictions of forces and moments in-plane with the crank were the most accurate (r = 0.95-0.97). The TCN model could predict external loads during activities of different intensities, making it viable for different exercise protocols. The ability to predict the loads associated with forward propulsion using wearable-type data enables the development of informed exercise guidelines.

Player Activity and Load Profiling with Hidden Markov Models: A Novel Application in Rugby League.

Player movement in rugby league is complex, being spatiotemporal and multifaceted. Modeling this complexity to provide robust measures of player activity and load has proved difficult, with important aspects of player movement yet to be considered. These include the influence of time-varying covariates on player activity and the combination of different dimensions of player movement. Few studies have simultaneously categorized player activity into different activity states and investigated factors influencing the transition between states, or compared player activity and load profiles between matches and training. This study applied hidden Markov models (HMMs)-a data-driven, multivariate approach-to rugby league training and match GPS data to i) demonstrate how HMMs can combine multiple variables in a data-driven way to effectively categorize player movement states, ii) investigate the influence of two time-varying covariates, score difference and elapsed match time on player activity states, and iii) compare player activity and load profiles within and between training and match modalities. HMMs were fitted to player GPS, accelerometer and heart rate data of one English Super League team across 60 training sessions and 35 matches. Distinct activity states were detected for both matches and training, with transitions between states in matches influenced by score difference and elapsed time and clear differences in activity and load profiles between training and matches. HMMs can model the complexity of player movement to effectively profile player activity and load in rugby league and have the potential to facilitate new research across several sports.

We successfully derived player activity and load profiles in both training and match contexts in a data-driven and multivariate way using hidden Markov models.HMMs can be used to investigate the probability of changing between activity states as a function of time-varying covariates, augmenting current activity profiling practice.We discovered key differences between the activity and load profiles between training and matches in rugby league. In particular, a very directed high-speed running state in training that is seldom accessed by players in matches.We demonstrated how visualizing the output of HMMs can provide decision support by facilitating comparisons of activity and load profiles within and between players in matches and training.We posit that the methodology detailed in this paper can become a standardized approach to player activity and load profiling based on player movement data across multiple sports because it is flexible, data-driven, multivariate and statistically robust.

Weekly External Load Correlation in Season Microcycles with Game Running Performance and Training Quantification in Elite Young Soccer Players.

The purpose of this study was to (a) correlate the weekly external training load with the game running performance in season microcycles and (b) specify the optimal training/game ratio of the weekly external load in elite youth soccer players. The total distance (TD), the high-speed running distance (HSRD) (19.8-25.2 km/h), the ZONE6 distance (>25.2 km/h), the acceleration (ACC) (≥+2 m/s2), and the deceleration (DEC) (≥-2 m/s2) were monitored with global positioning system (GPS) technology throughout 18 microcycles and official games. TD had a very high positive correlation average (r = 0.820, p = 0.001), the HSRD had a high positive correlation average (r = 0.658, p = 0.001), the ZONE6 distance and DEC had a moderate positive correlation average ((r = 0.473, p = 0.001) and (r = 0.478, p = 0.001), respectively), and the ACC had a low positive correlation average (r = 0.364, p = 0.001) between microcycles and games. Regarding the training/game ratio, the HSRD showed statistically significant differences between ratios 1.43 and 2.60 (p = 0.012, p ≤ 0.05), the ACC between ratios 2.42 and 4.45 (p = 0.050, p ≤ 0.05) and ratios 3.29 and 4.45 (p = 0.046, p ≤ 0.05), and the DEC between ratios 2.28 and 3.94 (p = 0.034, p ≤ 0.05). Considering the correlation between weekly training and game external load, high weekly training TD values correspond to higher game values, whereas HSRD, ZONE6 distance, ACC, and DEC, which determine training intensity, should be trained in a specific volume. Training/game ratios of 1.43, 2.42 to 3.29, and 2.28 to 3.11 seem to be optimal for HSRD, ACC, and DEC weekly training, respectively.

A new approach to comparing the demands of small-sided games and soccer matches.

To improve soccer performance, coaches should be able to replicate the match's physical efforts during the training sessions. For this goal, small-sided games (SSGs) are widely used. The main purpose of the current study was to develop similarity and overload scores to quantify the degree of similarity and the extent to which the SSG was able to replicate match intensity. GPSs were employed to collect external load and were grouped in three vectors (kinematic, metabolic, and mechanical). Euclidean distance was used to calculate the distance between training and match vectors, which was subsequently converted into a similarity score. The average of the pairwise difference between vectors was used to develop the overload scores. Three similarity (Simkin, Simmet, Simmec) and three overload scores (OVERkin, OVERmet, OVERmec) were defined for kinematic, metabolic, and mechanical vectors. Simmet and OVERmet were excluded from further analysis, showing a very large correlation (r > 0.7, p < 0.01) with Simkin and OVERkin. The scores were subsequently analysed considering teams' level (First team vs. U19 team) and SSGs' characteristics in the various playing roles. The independent-sample t-test showed (p < 0.01) that the First team presented greater Simkin (d = 0.91), OVERkin (d = 0.47), and OVERmec (d = 0.35) scores. Moreover, a generalized linear mixed model (GLMM) was employed to evaluate differences according to SSG characteristics. The results suggest that a specific SSG format could lead to different similarity and overload scores according to the playing position. This process could simplify data interpretation and categorize SSGs based on their scores.

Physical and Physiological Demands of Amateur Portuguese Field and Assistant Football Referees.

Referees are crucial elements in football, and they must meet the physical and physiological demands each match poses to them. The aim is to analyse the physical and physiological demands of amateur referees in games at the regional level (4th division), examining the differences between the first and second halves and between assistant (age: 25.10 ± 4.97) and main referees (age: 25.65 ± 5.12). A total of 29 matches were analysed with GPS devices, and internal and external load metrics were analysed. Overall, main referees, due to their central role in game management, showed higher levels of physical and physiological load than assistant referees, with more high-intensity activities, greater distance covered and higher heart rate. The results also revealed that there were no differences between the halves for total distance covered for either the main or assistant referees. However, the main referees covered a greater distance in high-intensity running during the first half (p = 0.05; d = 0.389). These findings emphasise the importance of tailored training protocols to enhance performance and reduce fatigue-related errors, highlighting the significance of endurance, high-intensity running ability, and strategies to manage transient fatigue in referee preparation.

Monitoring Within-Individual Dose-Response Relationships in Professional Soccer Players: The Importance of Fitness Level.

PURPOSE: To (1) examine within-individual player dose-response associations between selected training-load measures and changes in aerobic fitness level via submaximal exercise heart rate (HRex%) and (2) measure the relationships between these dose-response associations with basal HRex% (to study the influence of fitness level on dose-response relationship). METHODS: During an in-season phase, selected training-load measures including total minutes, total distance, mechanical work (the sum number of accelerations and decelerations > 3 m2), high metabolic load distance, and Edwards' training impulse were collected via Global Positioning System and heart-rate sensors for analyzing accumulated load. A submaximal warm-up test was used repeatedly before and after 9 phases to elicit HRex% and track fitness changes at an individual level. RESULTS: Negative to positive extensive ranges of within-individual associations were found among players for different metrics (r = -.84 to .89). The relationship between pooled HRex% (basal fitness) and dose-response correlations showed inverse very large (r = -.71) and large (r = -.65) values for accumulated weekly minutes and distance. However, moderate values were found for all other measures (r = -.35 to -.42). CONCLUSIONS: Individual players show extensive different ranges of dose-response associations with training measures. The dose-response association is influenced by players' fitness level, and players with lower fitness levels show stronger inverse relationships with accumulated minutes and total distance.