Importance of Various Training-Load Measures in Injury Incidence of Professional Rugby League Athletes

2017 ◽  
Vol 12 (6) ◽  
pp. 819-824 ◽  
Author(s):  
Heidi R. Thornton ◽  
Jace A. Delaney ◽  
Grant M. Duthie ◽  
Ben J. Dascombe
Keyword(s):  
Risk Factors ◽  
High Speed ◽  
Injury Risk ◽  
Perceived Exertion ◽  
Rugby League ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Machine Learning Techniques ◽  
Injury Incidence ◽  
The Individual

Purpose:To investigate the ability of various internal and external training-load (TL) monitoring measures to predict injury incidence among positional groups in professional rugby league athletes.Methods:TL and injury data were collected across 3 seasons (2013–2015) from 25 players competing in National Rugby League competition. Daily TL data were included in the analysis, including session rating of perceived exertion (sRPE-TL), total distance (TD), high-speed-running distance (>5 m/s), and high-metabolic-power distance (HPD; >20 W/kg). Rolling sums were calculated, nontraining days were removed, and athletes’ corresponding injury status was marked as “available” or “unavailable.” Linear (generalized estimating equations) and nonlinear (random forest; RF) statistical methods were adopted.Results:Injury risk factors varied according to positional group. For adjustables, the TL variables associated most highly with injury were 7-d TD and 7-d HPD, whereas for hit-up forwards they were sRPE-TL ratio and 14-d TD. For outside backs, 21- and 28-d sRPE-TL were identified, and for wide-running forwards, sRPE-TL ratio. The individual RF models showed that the importance of the TL variables in injury incidence varied between athletes.Conclusions:Differences in risk factors were recognized between positional groups and individual athletes, likely due to varied physiological capacities and physical demands. Furthermore, these results suggest that robust machine-learning techniques can appropriately monitor injury risk in professional team-sport athletes.

scholarly journals Multiple Measures are Needed to Quantify Training Loads in Professional Rugby League

2017 ◽  
Vol 38 (10) ◽  
pp. 735-740 ◽  
Author(s):  
Daniel Weaving ◽  
Ben Jones ◽  
Phil Marshall ◽  
Kevin Till ◽  
Grant Abt
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
High Speed ◽  
Perceived Exertion ◽  
Principal Component ◽  
Component Analysis ◽  
Rugby League ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Training Mode

AbstractThis study aims to investigate the effect of training mode (conditioning and skills) on multivariate training load relationships in professional rugby league via principal component analysis. Four measures of training load (internal: heart rate exertion index, session rating of perceived exertion; external: PlayerLoad™, individualised high-speed distance) were collected from 23 professional male rugby league players over the course of one 12 wk preseason period. Training was categorised by mode (skills or conditioning) and then subjected to a principal component analysis. Extraction criteria were set at an eigenvalue of greater than 1. Modes that extracted more than 1 principal component were subject to a varimax rotation. Skills extracted 1 principal component, explaining 57% of the variance. Conditioning extracted 2 principal components (1st: internal; 2nd: external), explaining 85% of the variance. The presence of multiple training load dimensions (principal components) during conditioning training provides further evidence of the influence of training mode on the ability of individual measures of external or internal training load to capture training variance. Consequently, a combination of internal and external training-load measures is required during certain training modes.

scholarly journals Combining Internal- and External-Training-Load Measures in Professional Rugby League

2014 ◽  
Vol 9 (6) ◽  
pp. 905-912 ◽  
Author(s):  
Dan Weaving ◽  
Phil Marshall ◽  
Keith Earle ◽  
Alan Nevill ◽  
Grant Abt
Keyword(s):  
Principal Components ◽  
High Speed ◽  
Perceived Exertion ◽  
Principal Component ◽  
Rugby League ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Training Dose ◽  
External Training ◽  
Training Impulse

Purpose:This study investigated the effect of training mode on the relationships between measures of training load in professional rugby league players.Methods:Five measures of training load (internal: individualized training impulse, session rating of perceived exertion; external—body load, high-speed distance, total impacts) were collected from 17 professional male rugby league players over the course of two 12-wk preseason periods. Training was categorized by mode (small-sided games, conditioning, skills, speed, strongman, and wrestle) and subsequently subjected to a principal-component analysis. Extraction criteria were set at an eigenvalue of greater than 1. Modes that extracted more than 1 principal component were subjected to a varimax rotation.Results:Small-sided games and conditioning extracted 1 principal component, explaining 68% and 52% of the variance, respectively. Skills, wrestle, strongman, and speed extracted 2 principal components each explaining 68%, 71%, 72%, and 67% of the variance, respectively.Conclusions:In certain training modes the inclusion of both internal and external training-load measures explained a greater proportion of the variance than any 1 individual measure. This would suggest that in training modes where 2 principal components were identified, the use of only a single internal or external training-load measure could potentially lead to an underestimation of the training dose. Consequently, a combination of internal- and external-load measures is required during certain training modes.

Within-Subject Correlation Between Salivary IgA and Measures of Training Load in Elite Football Players

2019 ◽  
Vol 14 (6) ◽  
pp. 847-849 ◽  
Author(s):  
Pedro Figueiredo ◽  
George P. Nassis ◽  
João Brito
Keyword(s):  
High Speed ◽  
Perceived Exertion ◽  
Immunoglobulin A ◽  
Correlation Coefficients ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Football Players ◽  
Positioning Systems ◽  
External Training ◽  
Salivary Secretory Immunoglobulin A

Purpose: To quantify the association between salivary secretory immunoglobulin A (sIgA) and training load in elite football players. Methods: Data were obtained on 4 consecutive days during the preparation camp for the Rio 2016 Olympic Games. Saliva samples of 18 elite male football players were collected prior to breakfast. The session rating of perceived exertion (s-RPE) and external training-load metrics from global positioning systems (GPS) were recorded. Within-subject correlation coefficients between training load and sIgA concentration, and magnitude of relationships, were calculated. Results: sIgA presented moderate to large negative correlations with s-RPE (r = −.39), total distance covered (r = −.55), accelerations (r = −.52), and decelerations (r = −.48). Trivial to small associations were detected between sIgA and distance covered per minute (r = .01), high-speed distance (r = −.23), and number of sprints (r = −.18). sIgA displayed a likely moderate decrease from day 1 to day 2 (d = −0.7) but increased on day 3 (d = 0.6). The training-load variables had moderate to very large rises from day 1 to day 2 (d = 0.7 to 3.2) but lowered from day 2 to day 3 (d = −5.0 to −0.4), except for distance per minute (d = 0.8) and sprints (unclear). On day 3, all training-load variables had small to large increments compared with day 1 (d = 0.4 to 1.5), except for accelerations (d = −0.8) and decelerations (unclear). Conclusions: In elite football, sIgA might be more responsive to training volume than to intensity. External load such as GPS-derived variables presented stronger association with sIgA than with s-RPE. sIgA can be used as an additional objective tool in monitoring football players.

scholarly journals We know they train, but what do they do? Implications for coaches working with adolescent rugby union players

2017 ◽  
Vol 12 (2) ◽  
pp. 175-182 ◽  
Author(s):  
Padraic J Phibbs ◽  
Ben Jones ◽  
Gregory AB Roe ◽  
Dale B Read ◽  
Joshua Darrall-Jones ◽  
...  
Keyword(s):  
Heart Rate ◽  
High Speed ◽  
Injury Risk ◽  
Perceived Exertion ◽  
Rugby Union ◽  
Rating Of Perceived Exertion ◽  
Limited Information ◽  
Positioning Systems ◽  
Global Positioning ◽  
School Club

Limited information is available regarding the training loads of adolescent rugby union players. One-hundred and seventy male players (age 16.1 ± 1.0 years) were recruited from 10 teams representing two age categories (under-16 and under-18) and three playing standards (school, club and academy). Global positioning systems, accelerometers, heart rate and session-rating of perceived exertion (s-RPE) methods were used to quantify mean session training loads. Session demands differed between age categories and playing standards. Under-18 academy players were exposed to the highest session training loads in terms of s-RPE (236 ± 42 AU), total distance (4176 ± 433 m), high speed running (1270 ± 288 m) and PlayerLoad™ (424 ± 56 AU). Schools players had the lowest session training loads in both respective age categories. Training loads and intensities increased with age and playing standard. Individual monitoring of training load is key to enable coaches to maximise player development and minimise injury risk.

The Impact of 3 Different-Length Between-Matches Microcycles on Training Loads in Professional Rugby League Players

2015 ◽  
Vol 10 (6) ◽  
pp. 767-773 ◽  
Author(s):  
Alexandre Moreira ◽  
Tom Kempton ◽  
Marcelo Saldanha Aoki ◽  
Anita C. Sirotic ◽  
Aaron J. Coutts
Keyword(s):  
Perceived Exertion ◽  
Training Session ◽  
Rugby League ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Endurance Athletes ◽  
Moderate Intensity ◽  
High Intensity Zone ◽  
Competition Period ◽  
The Impact

Purpose: To examine the impact of varying between-matches microcycles on training characteristics (ie, intensity, duration, and load) in professional rugby league players and to report on match load related to these between-matches microcycles. Methods: Training-load data were collected during a 26-wk competition period of an entire season. Training load was measured using the session rating of perceived exertion (session-RPE) method for every training session and match from 44 professional rugby league players from the same National Rugby League team. Using the category-ratio 10 RPE scale, the training intensity was divided into 3 zones (low <4 AU, moderate ≥4-≤7 AU, and high >7 AU). Three different-length between-matches recovery microcycles were used for analysis: 5−6 d, 7−8 d, and 9−10 d. Results: A total of 3848 individual sessions were recorded. During the shorter-length between-matches microcycles (5−6 d), significantly lower training load was observed. No significant differences for subsequent match load or intensity were identified between the various match recovery periods. Overall, 16% of the training sessions were completed at the low-intensity zone, 61% at the moderate-intensity zone, and 23% at the high-intensity zone. Conclusions: The findings demonstrate that rugby league players undertake higher training load as the length of between-matches microcycles is increased. The majority of in-season training of professional rugby league players was at moderate intensity, and a polarized approach to training that has been reported in elite endurance athletes does not occur in professional rugby league.

scholarly journals In-season internal and external training load quantification of an elite European soccer team

2018 ◽  
Author(s):  
Rafael Soares Oliveira ◽  
João Paulo Brito ◽  
Alexandre Martins ◽  
Bruno Mendes ◽  
Francisco Calvete ◽  
...  
Keyword(s):  
High Speed ◽  
Perceived Exertion ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Soccer Players ◽  
Soccer Teams ◽  
External Training ◽  
Daily Training ◽  
Player Positions ◽  
Level Of Effort

Elite soccer teams that participate in European competitions often have a difficult schedule, involving weeks in which they play up to three matches, which leads to acute and transient subjective, biochemical, metabolic and physical disturbances in players over the subsequent hours and days. Inadequate time recovery between matches can expose players to the risk of training and competing whilst not fully recovered. Controlling the level of effort and fatigue of players to reach higher performances during the matches is therefore critical. Therefore, the aim of the current study was to provide the first report of seasonal internal and external training load (TL) that included Hooper Index (HI) scores in elite soccer players during an in-season period. Sixteen elite soccer players were sampled, using global position system, session rating of perceived exertion (s-RPE) and HI scores during the daily training sessions throughout the 2015-2016 in-season period. Data were analysed across ten mesocycles (M: 1 to 10) and collected according to the number of days prior to a match. Total daily distance covered was higher at the start (M1 and M3) compared to the final mesocycle (M10) of the season. M1 (5589m) reached a greater distance than M5 (4473m) (ES = 9.33 [12.70, 5.95]) and M10 (4545m) (ES = 9.84 [13.39, 6.29]). M3 (5691m) reached a greater distance than M5 (ES = 9.07 [12.36, 5.78]), M7 (ES = 6.13 [8.48, 3.79]) and M10 (ES = 9.37 [12.76, 5.98]). High-speed running distance was greater in M1 (227m), than M5 (92m) (ES = 27.95 [37.68, 18.22]) and M10 (138m) (ES = 8.46 [11.55, 5.37]). Interestingly, the s-RPE response was higher in M1 (331au) in comparison to the last mesocycle (M10, 239au). HI showed minor variations across mesocycles and in days prior to the match. Every day prior to a match, all internal and external TL variables expressed significant lower values to other days prior to a match (p<0.01). In general, there were no differences between player positions. Conclusions: Our results reveal that despite the existence of some significant differences between mesocycles, there were minor changes across the in-season period for the internal and external TL variables used. Furthermore, it was observed that MD-1 presented a reduction of external TL (regardless of mesocycle) while internal TL variables did not have the same record during in-season match-day-minus.

Internal Training Load Measures in Elite Adolescent Ballet Dancers

2020 ◽  
Vol 24 (4) ◽  
pp. 175-182
Author(s):  
Valeriya G. Volkova ◽  
Amanda M. Black ◽  
Sarah J. Kenny
Keyword(s):  
Heart Rate ◽  
Injury Risk ◽  
Perceived Exertion ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
High Risk Population ◽  
Heart Rate Data ◽  
Ballet Dancers ◽  
Adolescent Dancers ◽  
Training Impulse

Training load has been identified as a risk factor for musculoskeletal injury in sport, but little is known about the effects of training load in dance. The purpose of this study was to describe adolescent dancers' internal training load (ITL) and compare objective and subjective measures of ITL using heart rate (HR) training impulse methods and session Rating of Perceived Exertion (sRPE), respectively. Fifteen female elite adolescent ballet dancers at a vocational dance school volunteered to participate in the study. Internal training load data using HR and sRPE were collected over 9 days of multiple technique classes at the midpoint of the dancers' training year. Heart rate data were quantified using Edwards' training impulse (ETRIMP) and Banister's training impulse (BTRIMP), and sRPE was estimated from the modified Borg 0 to 10 scale and class duration. Descriptive statistics (median [M], and interquartile range [IQR]) were determined in arbitrary units (AU), and were as follows for all classes combined: ETRIMP: M = 134 AU (IQR = 79 to 244 AU); BTRIMP: M = 67 AU (IQR = 38 to 109); sRPE: M = 407 AU (IQR = 287 to 836 AU). The association and agreement between objective and subjective ITL measures in ballet and pointe class was assessed using Spearman correlations (rs) and adjusted Bland-Altman 95% limits of agreement (LOA), respectively, with alpha set at 0.05. A significant moderate positive correlation was found between ETRIMP and BTRIMP in pointe class (rρ = 0.8000, p = 0.0031). The mean difference (LOA) between ETRIMP and BTRIMP was 121 AU (33 to 210 AU) in ballet and 43 AU (-3 to 88 AU) in pointe. It is concluded that some, but not all, measures of ITL in elite adolescent ballet dancers are comparable. Additional research is needed to examine the utilization of ITL measures for evaluating dance-related injury risk, as well as the application of ITL to inform the development of effective injury prevention strategies for this high-risk population.

scholarly journals Monitoring What Matters: A Systematic Process for Selecting Training-Load Measures

2017 ◽  
Vol 12 (s2) ◽  
pp. S2-101-S2-106 ◽  
Author(s):  
Sean Williams ◽  
Grant Trewartha ◽  
Matthew J. Cross ◽  
Simon P.T. Kemp ◽  
Keith A. Stokes
Keyword(s):  
Injury Risk ◽  
Perceived Exertion ◽  
Principal Component ◽  
Rugby Union ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Systematic Process ◽  
Daily Training ◽  
Monitoring Training ◽  
Generalized Linear Mixed Effects

Purpose:Numerous derivative measures can be calculated from the simple session rating of perceived exertion (sRPE), a tool for monitoring training loads (eg, acute:chronic workload and cumulative loads). The challenge from a practitioner’s perspective is to decide which measures to calculate and monitor in athletes for injury-prevention purposes. The aim of the current study was to outline a systematic process of data reduction and variable selection for such training-load measures.Methods:Training loads were collected from 173 professional rugby union players during the 2013–14 English Premiership season, using the sRPE method, with injuries reported via an established surveillance system. Ten derivative measures of sRPE training load were identified from existing literature and subjected to principal-component analysis. A representative measure from each component was selected by identifying the variable that explained the largest amount of variance in injury risk from univariate generalized linear mixed-effects models.Results:Three principal components were extracted, explaining 57%, 24%, and 9% of the variance. The training-load measures that were highly loaded on component 1 represented measures of the cumulative load placed on players, component 2 was associated with measures of changes in load, and component 3 represented a measure of acute load. Four-week cumulative load, acute:chronic workload, and daily training load were selected as the representative measures for each component.Conclusions:The process outlined in the current study enables practitioners to monitor the most parsimonious set of variables while still retaining the variation and distinct aspects of “load” in the data.

scholarly journals Relationship between External Load and Perceptual Responses to Training in Professional Football: Effects of Quantification Method

Sports ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 68 ◽  
Author(s):  
Vincenzo Rago ◽  
João Brito ◽  
Pedro Figueiredo ◽  
Peter Krustrup ◽  
António Rebelo
Keyword(s):  
High Speed ◽  
Perceived Exertion ◽  
Training Session ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Quantification Method ◽  
Individual Fitness ◽  
Football Team ◽  
Maximal Aerobic Speed ◽  
Sprinting Speed

We examined the within-player correlation between external training load (ETL) and perceptual responses to training in a professional male football team (n = 13 outfield players) over an eight-week competitive period. ETL was collected using 10-Hz GPS, whereas perceptual responses were accessed through rating of perceived exertion (RPE) questionnaires. Moderate-speed running (MSR), high-speed running (HSR) and sprinting were defined using arbitrary (fixed) and individualised speed zones (based on maximal aerobic speed and maximal sprinting speed). When ETL was expressed as actual distance covered within the training session, perceptual responses were moderately correlated to MSR and HSR quantified using the arbitrary method (p < 0.05; r = 0.53 to 0.59). However, the magnitude of correlations tended to increase when the individualised method was used (p < 0.05; r = 0.58 to 0.67). Distance covered by sprinting was moderately correlated to perceptual responses only when the individualised method was used (p < 0.05; 0.55 [0.05; 0.83] and 0.53 [0.02; 0.82]). Perceptual responses were largely correlated to the sum of distance covered within all three speed running zones, irrespective of the quantification method (p < 0.05; r = 0.58 to 0.68). When ETL was expressed as percentage of total distance covered within the training session, no significant correlations were observed (p > 0.05). Perceptual responses to training load seem to be better associated with ETL, when the latter is adjusted to individual fitness capacities. Moreover, reporting ETL as actual values of distance covered within the training session instead of percentual values inform better about players’ perceptual responses to training load.

A Comparison of Methods to Quantify the In-Season Training Load of Professional Soccer Players

2013 ◽  
Vol 8 (2) ◽  
pp. 195-202 ◽  
Author(s):  
Brendan R. Scott ◽  
Robert G. Lockie ◽  
Timothy J. Knight ◽  
Andrew C. Clark ◽  
Xanne A.K. Janse de Jonge
Keyword(s):  
High Speed ◽  
Perceived Exertion ◽  
Training Load ◽  
Rating Of Perceived Exertion ◽  
Soccer Players ◽  
Accelerometer Data ◽  
Professional Soccer ◽  
Significant Relationships ◽  
Global Positioning ◽  
Individual Training

Purpose:To compare various measures of training load (TL) derived from physiological (heart rate [HR]), perceptual (rating of perceived exertion [RPE]), and physical (global positioning system [GPS] and accelerometer) data during in-season field-based training for professional soccer.Methods:Fifteen professional male soccer players (age 24.9 ± 5.4 y, body mass 77.6 ± 7.5 kg, height 181.1 ± 6.9 cm) were assessed in-season across 97 individual training sessions. Measures of external TL (total distance [TD], the volume of low-speed activity [LSA; <14.4 km/h], high-speed running [HSR; >14.4 km/h], very high-speed running [VHSR; >19.8 km/h], and player load), HR and session-RPE (sRPE) scores were recorded. Internal TL scores (HR-based and sRPE-based) were calculated, and their relationships with measures of external TL were quantified using Pearson product–moment correlations.Results:Physical measures of TD, LSA volume, and player load provided large, significant (r = .71−.84; P < .01) correlations with the HR-based and sRPE-based methods. Volume of HSR and VHSR provided moderate to large, significant (r = .40−.67; P < .01) correlations with measures of internal TL.Conclusions:While the volume of HSR and VHSR provided significant relationships with internal TL, physical-performance measures of TD, LSA volume, and player load appear to be more acceptable indicators of external TL, due to the greater magnitude of their correlations with measures of internal TL.

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