Modelling Movement Energetics Using Global Positioning System Devices in Contact Team Sports: Limitations and Solutions

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.

Machine learning prediction of pulmonary oxygen uptake from muscle oxygen in cycling

The purpose of this study was to test whether a machine learning model can accurately predict VO2 across different exercise intensities by combining muscle oxygen (MO2) with heart rate (HR). Twenty young highly trained athletes performed the following tests: a ramp incremental exercise, three submaximal constant intensity exercises, and three severe intensity exhaustive exercises. A Machine Learning model was trained to predict VO2, with model inputs including heart rate, MO2 in the left (LM) and right legs (RM). All models demonstrated equivalent results, with the accuracy of predicting VO2 at different exercise intensities varying among different models. The LM+RM+HR model performed the best across all intensities, with low bias in predicted VO2 for all intensity exercises (0.08 ml/kg/min, 95% limits of agreement: -5.64 to 5.81), and a very strong correlation (r = 0.94, p < 0.001) with measured VO2. Furthermore, the accuracy of predicting VO2 using LM+HR or RM+HR was higher than using LM+RM, and higher than the accuracy of predicting VO2 using LM, RM, or HR alone. This study demonstrates the potential of a machine learning model combining MO2 and HR to predict VO2 with minimal bias, achieving accurate predictions of VO2 for different intensity levels of exercise.

A model for calculating the mechanical demands of overground running

An energy-based approach to quantifying the mechanical demands of overground, constant velocity and/or intermittent running patterns is presented. Total mechanical work done (Wtotal) is determined from the sum of the four sub components: work done to accelerate the centre of mass horizontally (Whor), vertically (Wvert), to overcome air resistance (Wair) and to swing the limbs (Wlimbs). These components are determined from established relationships between running velocity and running kinematics; and the application of work-energy theorem. The model was applied to constant velocity running (2-9 m/s), a hard acceleration event and a hard deceleration event. The estimated Wtotal and each sub component were presented as mechanical demand (work per unit distance) and power (work per unit time), for each running pattern. The analyses demonstrate the model is able to produce estimates that: 1) are principally determined by the absolute running velocity and/or acceleration; and 2) can be attributed to different mechanical demands given the nature of the running bout. Notably, the proposed model is responsive to varied running patterns, producing data that are consistent with established human locomotion theory; demonstrating sound construct validity. Notwithstanding several assumptions, the model may be applied to quantify overground running demands on flat surfaces.

Analysis of Motion Characteristics and Metabolic Power in Elite Male Handball Players

While handball is characterized by repeated sprints and changes of direction, traditional player load models do not consider accelerations and decelerations. The aim of this study was to analyze the differences between metabolic power and speed zones for player load assessment with regard to the player role. Position data from 330 male individuals during 77 games from the 2019/20 German Men's Handball-Bundesliga (HBL) were analyzed, resulting in 2233 individual observations. Players were categorized into wings, backs and pivots. Distance covered in different speed zones, metabolic power, metabolic work, equivalent distance (metabolic work divided by energy cost of running), time spend running, energy spend running, and time over 10 and 20 W were calculated. A 2-by-3 mixed ANOVA was calculated to investigate differences and interactions between groups and player load models. Results showed that total distance was longest in wings (3568 ± 1459 m in 42 ± 17 min), followed by backs (2462 ± 1145 m in 29 ± 14 min), and pivots (2445 ± 1052 m in 30 ± 13 min). Equivalent distance was greatest in wings (4072.50 ± 1644.83 m), followed by backs (2765.23 ± 1252.44 m), and pivots (2697.98 ± 1153.16 m). Distance covered and equivalent distance showed moderate to large interaction effects between wings and backs (p < .01, ES = 0.73) and between wings and pivots (p < .01, ES = 0.86) and a small interaction effect between backs and pivots (p < .01, ES = 0.22). The results underline the need for individualized management of training loads and the potential of using information about locomotive accelerations and decelerations to obtain more precise descriptions of player load during handball game performance at the highest level of competition. Future studies should investigate the influence of physical performance on smaller match sequences, like ball possession phases.

Metabolic Power in Team and Racquet Sports: A Systematic Review with Best-Evidence Synthesis

Background

In intermittent team and racquet sports, metabolic loads are rarely investigated as they are difficult to examine, e.g., by portable metabolic carts and lactate measures. However, determining the instantaneous metabolic power of intermittent running from acceleration and speed data is possible. Recently, this potential has gained more interest in research and practice due to the development of player tracking technologies that allow easy access to the required data. The aim of this review was to systematically investigate the validity and point out the evidence of this new approach for estimating metabolic loads in intermittent sports. To provide an in-depth understanding of this approach and its validity, the fundamental aspects of the underlying concept were also considered.

Methods

PubMed®, Cochrane Library, Web of Science™, and BISp-surf databases were included in the search conducted on March 1, 2021. Studies assessing physiological and methodological validation as well as conceptual studies of the metabolic power approach in intermittent sports players without diseases or injuries were deemed eligible. The quality assessment was implemented using a modified 12-item version of the Downs and Black checklist. Additionally, a best-evidence synthesis of the validation studies was performed to clarify the direction and strength of the evidence.

Results

Of 947 studies that were identified, 31 met the eligibility criteria of which 7 were physiological, 13 methodological validation, and 11 conceptual studies. Gold standards for validating the metabolic power approach were predominantly oxygen uptake with 6 and traditional running speed analysis with 8 studies for physiological and methodological validation, respectively. The best-evidence synthesis showed conflicting to strong and moderate to strong evidence for physiological and methodological validity of the approach, respectively. The conceptual studies revealed several modifications regarding the approach that need to be considered. Otherwise, incorrect implementation can occur.

Conclusions

Evidence of the physiological validity of the metabolic power approach ranged from conflicting to strong. However, this should be treated with caution as the validation studies were often partially implemented incorrectly as shown by the underlying concept studies. Moreover, strong evidence indicated that the approach is valid from a methodological perspective. Future studies must consider what the metabolic power approach can and cannot actually display.

A lack of research exists in studies concerning children, females, and team and racquet sports besides soccer and the application of more profound physiological approaches for the validation and assessment of metabolic power estimated by acceleration and speed data is needed.

Previous physiological validation studies are outdated as there have been adaptations concerning the metabolic power approach for estimating metabolic loads over recent years, and methodological validation studies revealing its superiority over the traditional running speed approach.

Distinction between walking and running, different terrains, as well as aerobic and anaerobic energy supply should be considered when assessing metabolic power in team and racquet sports.

Predicting Rugby League Tackle Outcomes Using Strength and Power Principal Components

PURPOSE

Tackling is a fundamental skill in collision sports such as rugby league. Given the complexity of tackling and multitude of strength and power variables available for analysis, this study aimed to predict tackle outcomes in professional rugby league based on strength and power principal components (PCs).

METHODS

Twenty-eight rugby league players participated in this study. Maximal strength was assessed via 1 repetition maximum on the back squat, bench press, and bench pull. Lower-body vertical and horizontal power were evaluated using a countermovement jump and standing broad jump. A postmatch analysis of 5 National Rugby League matches was conducted to examine tackling outcomes. PC analysis was performed on the strength and power assessments. The first PCs were retained in each analysis, and a series of Spearman rank-order correlations were conducted between the tackle outcomes and the retained PCs. The PCs significantly related to tackle outcomes were included in the multiple regression analyses to estimate their effect on tackle outcomes.

RESULTS

Strength PC was a significant predictor of play-the-ball speed in attack, accounting for 54% of the variance. Countermovement jump PC was a significant predictor of postcontact meters, explaining 19% of the variance.

CONCLUSIONS

These findings demonstrate that a range of tackle outcomes may be predicted from strength and power components. The coaching staff may choose to develop programs and testing designed to focus on these components, which may further develop players' tackle outcomes during competition.

Predicting Temporal Gait Kinematics From Running Velocity

ABSTRACT

Gray, A, Price, M, and Jenkins, D. Predicting temporal gait kinematics from running velocity. J Strength Cond Res 35(9): 2379-2382, 2021-The manner in which stride frequency (f) changes in response to running velocity (v) is well established. Notably, as running velocity increases, duty factor (d, the % of the stride in stance) decreases, concomitantly with higher stride frequencies. Mathematical descriptions of this relationship do not exist, limiting our ability to reasonably predict gait-based metrics from wearable technologies. Therefore, the purpose of this study was to establish prediction equations for stride frequency and duty factor from running velocity. On 2 occasions, 10 healthy men (aged, 21.1 ± 2.2 years) performed constant pace running efforts at 3, 4, 5, 6, 7, and 8 m·s-1 over a 10-m segment on a tartan athletics track. Running efforts were filmed using a digital video camera at 300 frames per second, from which stride duration, support duration, and swing duration were determined. Regression equations to predict stride frequency and duty factor from running velocity were established by curve fitting. Acceptable test-retest reliability for the video-based determination of stride frequency (intraclass correlation = 0.87; typical error of the measurement [TEM] = 0.01 Hz; coefficient of variation [CV] = 2.9%) and duty factor (r = 0.93; TEM = 1%; CV = 3.9%) were established. The relationship between stride frequency and running velocity was described by the following quadratic equation: f = 0.026·v2 - 0.111·v + 1.398 (r2 = 0.903). The relationship between duty factor and running velocity was described by the quadratic equation d = 0.004·v2 - 0.061·v + 0.50 (r2 = 0.652). The relationships between v and f and between v and d are consistent with previous observations. These equations may contribute broader locomotor models or serve as input variables in data fusion algorithms that enhance outputs from wearable technologies.

The Use of Wearable Sensor Technology to Detect Shock Impacts in Sports and Occupational Settings: A Scoping Review

Shock impacts during activity may cause damage to the joints, muscles, bones, or inner organs. To define thresholds for tolerable impacts, there is a need for methods that can accurately monitor shock impacts in real-life settings. Therefore, the main aim of this scoping review was to present an overview of existing methods for assessments of shock impacts using wearable sensor technology within two domains: sports and occupational settings. Online databases were used to identify papers published in 2010-2020, from which we selected 34 papers that used wearable sensor technology to measure shock impacts. No studies were found on occupational settings. For the sports domain, accelerometry was the dominant type of wearable sensor technology utilized, interpreting peak acceleration as a proxy for impact. Of the included studies, 28 assessed foot strike in running, head impacts in invasion and team sports, or different forms of jump landings or plyometric movements. The included studies revealed a lack of consensus regarding sensor placement and interpretation of the results. Furthermore, the identified high proportion of validation studies support previous concerns that wearable sensors at present are inadequate as a stand-alone method for valid and accurate data on shock impacts in the field.

A new energetics model for the assessment of the power-duration relationship during over-ground running

We evaluated the reliability of an over-ground running three-minute all-out test (3MT) and compared this to traditional multiple-visit testing to determine the critical speed (CS) and distance > CS (D´). Using a novel energetics model during the 3MT, critical power (CP) and work > CP (W´) were also evaluated for reliability and compared to the multiple-visit tests. Over-ground running speed was measured using Global Positioning Systems during fixed-speed trials on a 400 m track to exhaustion, at four intensities corresponding to: (i) maximal oxygen uptake (V˙O2max) (V_max), (ii) 110% V˙O2max(110%Vmax), (iii) Δ70% (i.e. 70% of the difference between gas exchange threshold and V_max) and (iv) Δ85%. The participants subsequently performed the 3MT across two days to determine its reliability. There were no differences between the multiple-visit testing and the 3MT for CS (P = 0.328) and D´ (P = 0.919); however, CP (P = 0.02) and W´ (P < 0.001) were higher in the 3MT. The reliability of the 3MT was stable (P > 0.05) between trials for all variables, with coefficient of variation ranging from 2.0-8.1%. The current over-ground energetics model can reliably estimate CP and W´ based on GPS speed data during the 3MT, which supports its use for most athletic training and monitoring purposes. The reliability of the over-ground running 3MT for power- and speed-related indices was sufficient to detect typical training adaptations; however, it may overestimate CP (∼ 25 W) and W´ (∼ 7 kJ) compared to multiple-visit tests.

A New Foot-Mounted Inertial Measurement System in Soccer: Reliability and Comparison to Global Positioning Systems for Velocity Measurements During Team Sport Actions

The aims of this study were to i) compare a foot-mounted inertial system (PlayerMaker™) to three commercially available Global Positioning Systems (GPS) for measurement of velocity-based metrics during team sport movements and ii) evaluate the inter-unit reliability of the PlayerMaker™. Twelve soccer players completed a soccer simulation, whilst wearing a PlayerMaker™ and three GPS (GPS#1, #2 and #3). A sub-sample (n = 7) also wore two PlayerMaker™ systems concurrently. The PlayerMaker™ measured higher (p < 0.05) total distance (518 ± 15 m) compared to GPS#1 (488 ± 15 m), GPS#2 (486 ± 15 m), and GPS#3 (501 ± 14 m). This was explained by greater (p < 0.05) distances in the 1.5-3.5 m/s zone (356 ± 24 m vs. 326 ± 26 m vs. 324 ± 18 m vs. 335 ± 24 m) and the 3.51-5.5 m/s zone (64 ± 18 m vs. 35 ± 5 vs. 43 ± 8 m vs. 41 ± 8 m) between the PlayerMaker™, GPS#1, GPS#2 and GPS#3, respectively. The PlayerMaker™ recorded higher (p < 0.05) distances while changing speed. There were no systematic differences (p > 0.05) between the two PlayerMaker™ systems. The PlayerMaker™ is reliable and records higher velocity and distances compared to GPS.

Activity Profile and Physical Performance of Match Play in Elite Futsal Players

Understanding the physical demands of futsal requires a precise quantification of the players’ activities during match play. This study aimed to (1) describe external load, identifying the differences between the first and second halves in official futsal matches; (2) identify the most important external workload metrics to profile the players; and (3) identify the collinearity between variables in the analysis of physical performance of futsal players. Match external load data were collected from male players (n = 28) in six games of the Final Eight of the Portuguese Futsal Cup 2018. The players increased the distance covered per minute at 12–18 km/h in the second half (p < 0.01). Dynamic stress load also increased in the second half (p = 0.01). The variables that best predicted the physical profile of each player were decelerations (predictor importance, PI = 1), walking (PI = 1), sprinting (PI = 1), jogging (PI = 0.997), total distance covered per minute (PI = 0.992), and metabolic power (PI = 0.989). Decelerations showed the highest association with the clusters levels (p < 0.001; PI = 1); this suggests decelerations as a potential candidate for best analyzing the physical load of futsal players. Overall, the data from this exploratory study suggest that distance covered per minute (m/min), number of sprints (>18 km/h), decelerations (greater than-2 m/s), and metabolic power (W/kg) are the variables that most discriminate the load intensity of elite futsal players.

Quantifying the Collision Dose in Rugby League: A Systematic Review, Meta-analysis, and Critical Analysis

BACKGROUND

Collisions (i.e. tackles, ball carries, and collisions) in the rugby league have the potential to increase injury risk, delay recovery, and influence individual and team performance. Understanding the collision demands of the rugby league may enable practitioners to optimise player health, recovery, and performance.

OBJECTIVE

The aim of this review was to (1) characterise the dose of collisions experienced within senior male rugby league match-play and training, (2) systematically and critically evaluate the methods used to describe the relative and absolute frequency and intensity of collisions, and (3) provide recommendations on collision monitoring.

METHODS

A systematic search of electronic databases (PubMed, SPORTDiscus, Scopus, and Web of Science) using keywords was undertaken. A meta-analysis provided a pooled mean of collision frequency or intensity metrics on comparable data sets from at least two studies.

RESULTS

Forty-three articles addressing the absolute (n) or relative collision frequency (n min^-1) or intensity of senior male rugby league collisions were included. Meta-analysis of video-based studies identified that forwards completed approximately twice the number of tackles per game than backs (n = 24.6 vs 12.8), whilst ball carry frequency remained similar between backs and forwards (n = 11.4 vs 11.2). Variable findings were observed at the subgroup level with a limited number of studies suggesting wide-running forwards, outside backs, and hit-up forwards complete similar ball carries whilst tackling frequency differed. For microtechnology, at the team level, players complete an average of 32.7 collisions per match. Limited data suggested hit-up and wide-running forwards complete the most collisions per match, when compared to adjustables and outside backs. Relative to playing time, forwards (n min^-1 = 0.44) complete a far greater frequency of collision than backs (n min^-1 = 0.16), with data suggesting hit-up forwards undertake more than adjustables, and outside backs. Studies investigating g force intensity zones utilised five unique intensity schemes with zones ranging from 2-3 g to 13-16 g. Given the disparity between device setups and zone classification systems between studies, further analyses were inappropriate. It is recommended that practitioners independently validate microtechnology against video to establish criterion validity.

CONCLUSIONS

Video- and microtechnology-based methods have been utilised to quantify collisions in the rugby league with differential collision profiles observed between forward and back positional groups, and their distinct subgroups. The ball carry demands of forwards and backs were similar, whilst tackle demands were greater for forwards than backs. Microtechnology has been used inconsistently to quantify collision frequency and intensity. Despite widespread popularity, a number of the microtechnology devices have yet to be appropriately validated. Limitations exist in using microtechnology to quantify collision intensity, including the lack of consistency and limited validation. Future directions include application of machine learning approaches to differentiate types of collisions in microtechnology datasets.

Exercise tolerance during flat over-ground intermittent running: modelling the expenditure and reconstitution kinetics of work done above critical power

Purpose

We compared a new locomotor-specific model to track the expenditure and reconstitution of work done above critical power (W´) and balance of W´ (W´_BAL) by modelling flat over-ground power during exhaustive intermittent running.

Method

Nine male participants completed a ramp test, 3-min all-out test and the 30–15 intermittent fitness test (30–15 IFT), and performed a severe-intensity constant work-rate trial (S_CWR) at the maximum oxygen uptake velocity (vV̇O_2max). Four intermittent trials followed: 60-s at vV̇O_2max + 50% Δ₁ (Δ₁ = vV̇O_2max − critical velocity [V_Crit]) interspersed by 30-s in light (S_L; 40% vV̇O_2max), moderate (S_M; 90% gas-exchange threshold velocity [V_GET]), heavy (S_H; V_GET + 50% Δ₂ [Δ₂ = V_Crit − V_GET]), or severe (S_S; vV̇O_2max − 50% Δ₁) domains. Data from Global Positioning Systems were derived to model over-ground power. The difference between critical and recovery power (D_CP), time constant for reconstitution of W´ (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau_{{W^{\prime}}}$$\end{document}τW′), time to limit of tolerance (T_LIM), and W´_BAL from the integral (W´_BALint), differential (W´_BALdiff), and locomotor-specific (OG-W´_BAL) methods were compared.

Results

The relationship between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau_{{W^{\prime}}}$$\end{document}τW′ and D_CP was exponential (r² = 0.52). The \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\tau_{{W^{{\prime}}}}$$\end{document}τW′ for S_L, S_M, and S_H trials were 119 ± 32-s, 190 ± 45-s, and 336 ± 77-s, respectively. Actual T_LIM in the 30–15 IFT (968 ± 117-s) compared closely to T_LIM predicted by OG-W´_BAL (929 ± 94-s, P > 0.100) and W´_BALdiff (938 ± 84-s, P > 0.100) but not to W´_BALint (848 ± 91-s, P = 0.001).

Conclusion

The OG-W´_BAL accurately tracked W´ kinetics during intermittent running to exhaustion on flat surfaces.

Metabolic Power: A Step in the Right Direction for Team Sports

The ability of the "metabolic power" model to assess the demands of team-sport activity has been the subject of some interest-and much controversy-in team-sport research. Because the cost of acceleration depends on the initial speed and the costs of acceleration and deceleration are not "equal and opposite," changes in speed must be accounted for when evaluating variable-speed locomotion. The purpose of this commentary is to address some of the misconceptions regarding "metabolic power," acknowledge its limitations, and highlight some of the benefits that energetic analysis offers over alternative approaches to quantifying the demands of team sports.