Improvement of Prediction of Noncontact Injury in Elite Australian Footballers With Repeated Exposure to Established High-Risk Workload Scenarios

2018 ◽  
Vol 13 (9) ◽  
pp. 1130-1135 ◽  
Author(s):  
Marcus J. Colby ◽  
Brian Dawson ◽  
Peter Peeling ◽  
Jarryd Heasman ◽  
Brent Rogalski ◽  
...  
Keyword(s):  
High Risk ◽  
Area Under Curve ◽  
Injury Risk ◽  
Perceived Exertion ◽  
Model Performance ◽  
Load Condition ◽  
Rating Of Perceived Exertion ◽  
Injury Incidence ◽  
Maximal Velocity ◽  
Operating Characteristics

Objectives: To assess the effect of multiple high-risk-scenario (HRS) exposures on noncontact injury prediction in elite Australian footballers. Design: Retrospective cohort study. Methods: Sessional workload data (session rating of perceived exertion, global positioning system–derived distance, sprint distance, and maximum velocity) from 1 club (N = 60 players) over 3 seasons were collated; several established HRSs were also defined. Accumulated HRS sessional exposures were calculated retrospectively (previous 1–8 wk). Noncontact injury data were documented. Univariate and multivariate Poisson regression models determined injury incidence rate ratios (IRRs) while accounting for moderating effects (preseason workload volume and playing experience). Model performance was evaluated using receiver operating characteristics (area under curve). Results: Very low (0–8 sessions: IRR = 5.76; 95% confidence interval [CI], 1.69–19.66) and very high (>15 sessions: IRR = 4.70; 95% CI, 1.49–14.87) exposures to >85% of an individual’s maximal velocity over the previous 8 wk were associated with greater injury risk compared with moderate exposures (11–12 sessions) and displayed the best model performance (area under curve = 0.64). A single session corresponding to a very low chronic load condition over the previous week for all workload variables was associated with increased injury risk, with sprint distance (IRR = 3.25; 95% CI, 1.95–5.40) providing the most accurate prediction model (area under curve = 0.63). Conclusions: Minimal exposure to high-velocity efforts (maximum speed exposure and sprint volume) was associated with the greatest injury risk. Being underloaded may be a mediator for noncontact injury in elite Australian football. Preseason workload and playing experience were not moderators of this effect.

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.

Internal workload and non-contact injury: a one-season study of five teams from the UEFA Elite Club Injury Study

2018 ◽  
Vol 52 (23) ◽  
pp. 1517-1522 ◽  
Author(s):  
Alan McCall ◽  
Gregory Dupont ◽  
Jan Ekstrand
Keyword(s):  
Injury Risk ◽  
Predictive Power ◽  
Perceived Exertion ◽  
Area Under The Curve ◽  
Rating Of Perceived Exertion ◽  
Operating Characteristics ◽  
Relative Risks ◽  
Risk Of Injury ◽  
Published Evidence ◽  
High Workload

BackgroundInternal workload (ie, from training and matches) is considered one of the most important injury risk factors for elite European football teams, however there is little published evidence to support this belief.ObjectiveWe examined the association and predictive power of internal workload and non-contact injuries.MethodsFive elite European teams, 171 players (age: 25.1±4.9 years; height: 181.6±6.7 cm; body mass: 77.5±7.2 kg) participated over one full competitive season. Using the session-rating of perceived exertion (s-RPE) method player’s internal workloads were calculated for acute week, week-to-week changes, cumulated weeks, chronic weeks and acute:chronic ratios and analysed for association with non-contact injury (using generalised estimating equations (GEE)). Associated variables from GEE analysis were categorised into very low to very high workload zones and checked for increased relative risks (RRs). Associated workload variables were also analysed for predictive power (receiver operating characteristics).ResultsAcute:chronic workload ratios at 1:3 and 1:4 weeks were associated with non-contact injury (P<0.05). Specifically, a greater risk of injury was found for players with an acute:chronic workload at 1:4 weeks of 0.97 to 1.38 (RR 1.68; 95% CI 1.02 to 2.78, likely harmful) and >1.38 (RR 2.13; 95% CI 1.21 to 3.77, very likely harmful) compared with players whose acute:chronic workload was 0.60 to 0.97. An acute:chronic workload 1:3 of >1.42 compared with 0.59 to 0.97 displayed a 1.94 times higher risk of injury (RR 1.90; 95% CI 1.08 to 3.36, very likely harmful). Importantly, acute:chronic workload at both 1:4 and 1:3 showed poor predictive power (area under the curve 0.53 to 0.58) despite previous reports and beliefs that it can predict injury.ConclusionsThis study provides evidence for the acute:chronic internal workload (measured using s-RPE) as a risk factor for non-contact injury in elite European footballers. However the acute:chronic workload, in isolation, should not be used to predict non-contact injury.

scholarly journals Protection Against Spikes in Workload With Aerobic Fitness and Playing Experience: The Role of the Acute:Chronic Workload Ratio on Injury Risk in Elite Gaelic Football

2017 ◽  
Vol 12 (3) ◽  
pp. 393-401 ◽  
Author(s):  
Shane Malone ◽  
Mark Roe ◽  
Dominic A. Doran ◽  
Tim J. Gabbett ◽  
Kieran D. Collins
Keyword(s):  
Aerobic Fitness ◽  
Injury Risk ◽  
Perceived Exertion ◽  
Reference Group ◽  
Time Trial ◽  
Rating Of Perceived Exertion ◽  
Football Players ◽  
Risk Of Injury ◽  
Increased Risk ◽  
Injury Protection

Purpose:To examine the association between combined session rating of perceived exertion (RPE) workload measures and injury risk in elite Gaelic footballers.Methods:Thirty-seven elite Gaelic footballers (mean ± SD age 24.2 ± 2.9 y) from 1 elite squad were involved in a single-season study. Weekly workload (session RPE multiplied by duration) and all time-loss injuries (including subsequent-wk injuries) were recorded during the period. Rolling weekly sums and wk-to-wk changes in workload were measured, enabling the calculation of the acute:chronic workload ratio by dividing acute workload (ie, 1-weekly workload) by chronic workload (ie, rolling-average 4-weekly workload). Workload measures were then modeled against data for all injuries sustained using a logistic-regression model. Odds ratios (ORs) were reported against a reference group.Results:High 1-weekly workloads (≥2770 arbitrary units [AU], OR = 1.63–6.75) were associated with significantly higher risk of injury than in a low-training-load reference group (<1250 AU). When exposed to spikes in workload (acute:chronic workload ratio >1.5), players with 1 y experience had a higher risk of injury (OR = 2.22) and players with 2–3 (OR = 0.20) and 4–6 y (OR = 0.24) of experience had a lower risk of injury. Players with poorer aerobic fitness (estimated from a 1-km time trial) had a higher injury risk than those with higher aerobic fitness (OR = 1.50–2.50). An acute:chronic workload ratio of (≥2.0) demonstrated the greatest risk of injury.Conclusions:These findings highlight an increased risk of injury for elite Gaelic football players with high (>2.0) acute:chronic workload ratios and high weekly workloads. A high aerobic capacity and playing experience appears to offer injury protection against rapid changes in workload and high acute:chronic workload ratios. Moderate workloads, coupled with moderate to high changes in the acute:chronic workload ratio, appear to be protective for Gaelic football players.

To Couple or not to Couple? For Acute:Chronic Workload Ratios and Injury Risk, Does it Really Matter?

2019 ◽  
Vol 40 (09) ◽  
pp. 597-600 ◽  
Author(s):  
Tim J. Gabbett ◽  
Billy Hulin ◽  
Peter Blanch ◽  
Paul Chapman ◽  
David Bailey
Keyword(s):  
Injury Risk ◽  
Perceived Exertion ◽  
Rating Of Perceived Exertion ◽  
Percentile Rank

AbstractWe examined the association between coupled and uncoupled acute:chronic workload ratios (ACWR) and injury risk in a cohort of 28 elite cricket fast bowlers (mean±SD age, 26±5 yr). Workloads were estimated using the session rating of perceived exertion (session-RPE). Coupled ACWRs were calculated using a 1-week acute workload and 4-week chronic workload (acute workload was included in the chronic workload calculation), whereas uncoupled ACWRs used the most recent 1-week acute workload and the prior 3-week chronic workload (acute workload was not included in the chronic workload calculation). A nearly perfect relationship (R2=0.99) was found between coupled and uncoupled ACWRs. Using a percentile rank method, no significant differences in injury risk were found between the coupled and uncoupled ACWR. Higher ACWRs were associated with increased injury likelihood for both coupled and uncoupled methods, however there were no significant differences in injury risk between coupled and uncoupled ACWRs. Our data demonstrates that both coupled and uncoupled ACWRs produce the same injury likelihoods. Furthermore, our results are consistent with previous studies: higher ACWRs are associated with greater risk, irrespective of whether acute and chronic workloads are coupled or uncoupled.

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.

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.

A deep learning approach to injury forecasting in NBA basketball

2021 ◽  
pp. 1-12
Author(s):  
Alexander Cohan ◽  
Jake Schuster ◽  
Jose Fernandez
Keyword(s):  
Deep Learning ◽  
Sports Medicine ◽  
Injury Risk ◽  
Small Sample Size ◽  
Model Performance ◽  
Imbalanced Data ◽  
Feature Learning ◽  
Small Sample ◽  
Injury Incidence ◽  
Learning Approaches

Predicting athlete injury risk has been a holy grail in sports medicine with little progress to date due to a variety of factors such as small sample sizes, significantly imbalanced data, and inadequate statistical approaches. Modeling approaches which are not able to account for the multiple interactions across factors can be misleading. We address the small sample size by collecting longitudinal data of NBA player injuries using publicly available data sources and develop a state of the art deep learning model, METIC, to predict future injuries based on past injuries, game activity, and player statistics. We evaluate model performance using metrics appropriate for imbalanced data and find that METIC performs significantly better than other traditional machine learning approaches. METIC uses feature learning to create interactive features which become meaningful in combination with each other. METIC can be used by practitioners and front offices to improve athlete management and reduce injury incidence, potentially saving sports teams millions in revenue due to reduced athlete injuries.

Absolute and Relative Load and Injury in Elite Junior Australian Football Players Over 1 Season

2020 ◽  
Vol 15 (4) ◽  
pp. 511-519 ◽  
Author(s):  
Timothy J.H. Lathlean ◽  
Paul B. Gastin ◽  
Stuart V. Newstead ◽  
Caroline F. Finch
Keyword(s):  
Injury Risk ◽  
Perceived Exertion ◽  
Fold Increase ◽  
Estimating Equation ◽  
Rating Of Perceived Exertion ◽  
Football Players ◽  
Injury Surveillance System ◽  
Relative Load ◽  
Risk Of Injury ◽  
Australian Football

Purpose: To investigate the association between training and match loads and injury in elite junior Australian football players over 1 competitive season. Methods: Elite junior Australian football players (n = 290, age 17.7 [0.3] y, range 16–18 y) were recruited from the under-18 state league competition in Victoria to report load and injury information. One-week load (session rating of perceived exertion multiplied by duration) and all time-loss injuries were reported using an online sport-injury surveillance system. Absolute load measures (weekly sums) enabled the calculation of relative measures such as the acute:chronic workload ratio. Load measures were modeled against injury outcome (yes/no) using a generalized estimating equation approach, with a 1-wk lag for injury. Results: Low (<300 arbitrary units [au]) and high (>4650 au) 1-wk loads were associated with significantly higher risk of injury. Furthermore, low (<100 au) and high (>850 au) session loads were associated with a higher risk of injury. High strain values (>13,000) were associated with up to a 5-fold increase in the odds of injury. There was a relatively flat-line association between the acute:chronic workload ratio and injury. Conclusions: This study is the first investigation of elite junior athletes demonstrating linear and nonlinear relationships between absolute and relative load measures and injury. Coaches should focus player loads on, or at least close to, the point at which injury risk starts to increase again (2214 au for 1-wk load and 458 au for session load) and use evidence-based strategies across the week and month to help reduce the risk of injury.

scholarly journals Injury Incidence and Workloads during congested Schedules in Football

2019 ◽  
Vol 41 (02) ◽  
pp. 75-81
Author(s):  
Kieran Howle ◽  
Adam Waterson ◽  
Rob Duffield
Keyword(s):  
External Load ◽  
Perceived Exertion ◽  
Incidence Rates ◽  
Rating Of Perceived Exertion ◽  
Injury Incidence ◽  
Maximal Heart Rate ◽  
Injury Rates ◽  
External Training ◽  
External Loads ◽  
And Training

AbstractThis study compared injury incidence and training loads between single and multi-match weeks, and seasons with and without congested scheduling. Measures of internal (session-Rating of Perceived Exertion × duration for training/match and % maximal heart rate) and external load (total, low-, high-, and very high-intensity running distances) along with injury incidence rates were determined from 42 players over 3 seasons; including 1 without and 2 (season 2 and 3) with regular multi-match weeks. Within-player analyses compared 1 (n=214) vs. 2-match (n=86) weeks (>75min in matches), whilst team data was compared between seasons. Total injury rates were increased during multi-match weeks (p=0.001), resulting from increased match and training injuries (50.3, 16.9/1000h). Between-season total injury rates were highest when congested scheduling was greatest in season 3 (27.3/1000h) and season 2 (22.7/1000h) vs. season 1 (14.1/1000h; p=0.021). All external load measures were reduced in multi-match weeks (p<0.05). Furthermore, all internal and external training loads were lowest in seasons with congestion (p<0.05). In conclusion, increased injury rates in training and matches exist. Total loads remain comparable between single and multi-match weeks, though reduce in congested seasons. Whether injuries result from reduced recovery, increased match exposure or the discreet match external loads remain to be elucidated.

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