Article

The Relationship Between Training Load and Injury in Men’s Professional Basketball Players

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Abstract

Purpose: To establish the relationship between the acute:chronic workload ratio and lower extremity overuse injuries in professional basketball players over the course of a competitive season. Methods: The acute:chronic workload ratio was determined by calculating the sum of the current week's session rate of perceived exertion (sRPE) training load (acute load) and dividing it by the average weekly training load over the previous four weeks (chronic load). All injuries were recorded weekly using a self-reported injury questionnaire (Oslo Sports Trauma Research Centre Injury Questionnaire(20)) Workload ratios were modelled against injury data using a logistic regression model with unique intercepts for each player. Results: Substantially fewer team members were injured following workload ratios between 1-1.49 (36%) compared to very low (≤0.5; 54%), low (0.5-0.99; 51%) or high (≥1.5; 59%) workload ratios. The regression model provided unique workload-injury trends for each player, but all mean differences in likelihood of being injured between workload ratios were unclear. Conclusions: Maintaining workload ratios between 1-1.5 may be optimal for athlete preparation in professional basketball. An individualized approach to modelling and monitoring the training load-injury relationship, along with a symptom-based injury-surveillance method, should help coaches and performance staff with individualized training load planning and prescription, and with developing athlete-specific recovery and rehabilitation strategies.

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... Currently, there is limited research investigating the workload-injury relationship in basketball. 6,7 Research from other team sports has found that both high and low weekly workloads (termed acute workload) are associated with a greater injury risk. [8][9][10][11] This has led to the suggestion that working within a certain workload range could minimize injury risk, while maximizing performance. ...
... 21,22 Only one study has investigated the relationship between workload and injury risk in basketball using the ACWR. 7 This study observed that an ACWR range of 1.0-1.49 was associated with the lowest injury risk compared to ACWR ranges above and below this threshold, however further statistical analysis did not reveal clear differences between injury risk for different ACWR categories. ...
... 24 An additional frailty model was run where ACWR was categorized into three groups (<1.0, 1.0-1.5, >1.5), using an ACWR range of 1.0-1.5 as the reference group, as previous research has suggested that this is the ACWR 'sweet spot' in professional basketball. 7 Resulting HRs below 1.0 indicated a lower injury risk relative to the reference group, whereas a HR above 1.0 indicated a higher injury risk relative to the reference group, and a HR of 1.0 indicated no difference. All data was analysed using Stata v15 (StataCorp, Texas, USA) with statistical significance set at p<0.05 throughout. ...
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This study aimed to investigate the relationship between workload and injury risk in professional men's basketball. Player workload (training and game) and injury data were collected from 16 elite basketball players belonging to a National Basketball League (NBL) team over one full competitive season. Trunk mounted tri-axial accelerometers were used to quantify player workload. Workload measures included cumulative loads (2-, 3-, and 4-weekly), acute workload (1 week workload), chronic load (4-week rolling average workload), absolute difference in acute workload, and acute:chronic workload ratio (ACWR) (acute workload divided by chronic workload). All workload measures were categorised as low, moderate, high, very high based on quartiles and modelled against injury data using a frailty model. Using low as the reference category a hazard ratio (HR) was calculated for each category to determine the relative risk of injury. There were no statistically significant differences in injury risk between high and low ACWR (HR = 1.98; p>0.05). Furthermore, no significant associations were detected between injury risk and different categories for 2 week accumulated workload (HR = 1.02 to 2.02; p>0.05), 3 week accumulated workload (HR = 0.22 to 1.21; p>0.05), 4 week accumulated workload (HR = 0.35 to 0.71; p>0.05) or absolute difference in acute workload (HR = 0.64 to 1.75; p>0.05). These findings do not support the use of any of the investigated workload measures to inform injury risk in a single men's professional basketball team. Further research, combining multiple squads to provide a larger sample, may provide a clearer picture.
... It provides a mechanistic model to identify the relationship between stress, adaptation, and fatigue (Cunanan et al., 2018) and consists of three stages (Figure 2.6). The initial stage, known as the alarm reaction, refers to the short-term negative effect that occurs following the onset of training stress (McGuigan, 2017). Providing adequate recovery ensues, the body will enter the second stage, termed the resistance stage. ...
... Injury definition varied across the included articles with seven studies ( definition of any injury that caused a player to seek medical or first aid treatment during or after a rugby league activity but did not lead to loss of further participation or non-selection for matches. Six studies (27.3%) (Delecroix et al., 2018;Esmaeili et al., 2018;Stares et al., 2018;Timoteo et al., 2018;Colby et al., 2017b;Weiss et al., 2017) referred to a different source and two studies (9.1%) (Sampson et al., 2018;Hulin et al., 2014) used their own definition of injury. ...
... The RA model of calculating ACWR considers the association between training load and injury as linear, meaning each acute period within the aggregated chronic period is deemed equal. Despite this, there is an abundance of literature supporting its use (Delecroix et al., 2018;Fanchini et al., 2018;McCall et al., 2018;Stares et al., 2018;Timoteo et al., 2018;Colby et al., 2017b;Murray et al., 2017b;Weiss et al., 2017;Hulin et al., 2016a;2016b;. In the early work, Hulin et al. (2014) found that 7:28 RA ACWR in cricket players was associated with an increased injury risk in the following week, for internal workload (RR = 2.2, 95% CI 1.91-2.53), ...
Thesis
Introduction: Amateur Rugby Union has an inherent risk of injury that is associated with detrimental effects on player welfare and team performance. The monitoring of players’ preparedness for, and response to, training has become an integral tool for coaches in injury risk management as it may aid in the prescription and design of training. A training monitoring system (TMS) should be both attainable and scientifically grounded, however, there is a paucity of information in relation to monitoring training at the amateur level and the inherent challenges this presents. Aim: The aim of this doctoral research was to explore the associations between subjective measures of training load (TL) and wellness with injury occurrence in match-play and training sessions in amateur rugby in Ireland. Fundamentally, this programme of research aimed to offer practical methods of monitoring training that has the potential to mitigate injury risk and, in turn, benefit the health and wellbeing of players. Methods: Five studies were conducted in this programme of research which: (1) systematically reviewed and critically appraised the existing relevant literature regarding associations between the acute:chronic workload ratio (ACWR), and injury in team sports (Chapter Three), (2) established the current training monitoring practices of practitioners working with in amateur Rugby Union clubs (Chapter Four), (3) developed and evaluated an online TMS (Chapter Five), examined methods of addressing missing TL using missing value imputation (MVI) (Chapter Six), and (5) explored possible associations between subjective self-reported measures of wellness, various training load metrics, and injury in amateur Rugby Union. Results: The findings of the systematic review support the association between the ACWR and non-contact injuries and its use as a valuable tool for monitoring TL as part of a larger scale multifaceted monitoring system that includes other proven methods. 72.7% of practitioners working with amateur Rugby Union clubs monitored training with the most common method being the session rate of perceived exertion (sRPE), used in 83.3% of monitoring systems. The 3 most prominent challenges to motoring training were found to be lack of player compliance, data inconsistency and match-day challenges. Practitioners should strive to keep missing TL data at a minimum, however imputing missing data with the Daily Team Mean (DTMean) was the most accurate MVI method of the twelve MWI methods examined. Lastly, logistic regression found significant, strong associations (odds ratio (OR) = 6.172, 95% CI = 0.254 – 0.473, p < 0.001) between the occurrence of injury and the summative score of overall wellness (0-day lag). Significant weak associations were found between the occurrence of injury and the majority of ACWR calculations when 3-day and 7-day injury lag periods were applied. Conclusion: The findings of this programme of research support the positive association between injury and both subjective wellness and TL. Monitoring training of amateur athletes has its own unique challenges and confounders (e.g., limited time with players, occupation of players, resources available). Practitioners must accept that due to the complexity of injury, a risk will always be present and instead focus on prescribing training that they deem will promote positive adaptations in a safe manner. However, a TMS consisting of subjective measures may mitigate injury risk in amateur Rugby Union by supporting decisions around training prescription.
... Most of the studies investigating the relationships between load and injury have been conducted on rugby, cricket, football and soccer, (Drew & Finch, 2016;Eckard et al., 2018;Griffin et al., 2020;Jones et al., 2017) but only two studies have been focused on basketball (Caparrós et al., 2018;Weiss et al., 2017). Considering that the type and the risk of injury vary between team sports, the results on this topic should not be extended reliably at all to basketball (Griffin et al., 2020) and the limited evidence in basketball should be strengthened. ...
... Preliminary results regarding the relationships between load markers and injuries in basketball were provided by Weiss et al. (2017), who indicated that maintaining an internal ACWR between 1.00 and 1.49 might be optimal for players preparation as injury risk is lower. However, it should be considered that these data were retrieved from only 6 players belonging to one professional basketball team and are limited to one competitive season. ...
... Despite the importance and increasing popularity of this topic, only one study has investigated the association between internal load and non-contact injury in basketball, concluding that a range of ACWR4 between 1.00 and 1.49 might be optimal for reducing injury risk (Weiss et al., 2017). Our contrasting results might be due to the different Table 2. Injury occurrence, exposure and injury incidence during all seasons (preparation period, Championship tournament and Play-off tournament) per training and match. ...
Article
This study examined the association and predictive ability of internal load markers (based on rating of perceived exertion, RPE) with non-contact injuries in basketball. 35 basketball players (age: 24 ± 6 years, stature: 196 ± 9 cm, body mass: 91 ± 12 kg) were prospectively followed during 1 or 2 seasons, during which non-contact injuries were recorded. Markers examined were: mean weekly RPE, weekly load, exposure, week-to-week load change, acute:chronic 1:2, 1:3, 1:4 load ratio. A generalized estimating equations analysis was used to determine association with non-contact injury in the subsequent week. Prediction was examined with receiver operating characteristic curve, area under the curve (AUC) and Youden index. No associations were found between load markers and non-contact injuries (all p > 0.05); load markers showed no injury predictive ability (AUC range: 0.468–537; Youden index range: 0.019–132). In conclusion, the load markers selected are not associated with non-contact injuries and they cannot be used to predict injuries in basketball.
... Using non-contact soft tissue as the outcome measure, a low acute:chronic value also represented the highest risk with a "very likely" harmful effect. While the evidence regarding the relationship between the acute:chronic workload ratio and injury has previously reported a sweet spot Gabbett, 2016a;Malone et al., 2017b;Weiss et al., 2017;Stares et al., 2018), there is evidence that spikes in load (as individually defined by each unique study: range >1.6 to >2.11) often represent the greatest increase in risk to athletes Hulin et al., 2016a;Hulin et al., 2016b;Murray et al., 2017b). Despite the findings of this study for all injury and non-contact soft tissue injury alone, mirroring one another, the absolute injury hazard of all injury was greater than that of non-contact soft tissue injury (2.1% vs 0.7% respectively). ...
... calculation uses 1 week acute loads and 4 week rolling averagesHulin et al., 2016a;Hulin et al., 2016b;Malone et al., 2016;Malone et al., 2017b;Bowen et al., 2017;Colby et al., 2017;Weiss, Allen, McGuigan and Whatman, 2017;Murray et al., 2017b). However, there have been a number of other potential calculation methods, time frames and mathematical complexities suggested since the method's inception (discussed in sections 2.7.9.2, 2.7.9.3 and 2.7.9.4 respectively). ...
... ssed in sections 2.7.9.2, 2.7.9.3 and 2.7.9.4 respectively). Since its first use, a number of studies have reported similar sweet spots, whereby the risk of injury is greater at the lower and higher end of acute:chronic ratios, with the following values representing the lowest risk in each respective study: 1.00-1.25(Malone et al., 2017b), 1.0-1.49(Weiss et al., 2017) and 0.6-1.5(Stares et al., 2018). Whether or not a sweet spot exists in acute:chronic values is inconclusive, however, what is clear is that values exceeding an acute:chronic score of 1.5 appear to cause a higher injury risk with a number of different exact values reported; >1.6 (Hulin et al., 2016a), >1.76 for total distance >1.77 for ...
Thesis
The rate of injury in professional rugby union is high compared to that of other team sports. As such, the need for injury mitigation strategies is evident. One emerging approach is the appropriate management of player load, with multiple studies across different sports demonstrating the association between load and injury risk. The aim of this thesis, therefore, is to build upon the small amount of work undertaken in rugby union to further our understanding of this modifiable risk factor to aid governing bodies and club practitioners make informed decisions around player loading patterns.
... The most common timeframe used was a 1-week acute training load and 4-week chronic training load. [9][10][11]17,18,[20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36] Other timeframes included a 3-day acute load and a 21 daychronic load, 19 or varying timeframes. [37][38][39][40] One study examined various timeframes for calculating ACWR, 37 utilizing 2-9 days for acute loads, and chronic loads varying from 14 to 35 days, while the other study ranged from 1 to 2 weeks for acute loads, and 3-8 weeks for chronic loads, but only utilized the 1:4 week ratio for relationship to injury. ...
... Of the 18 articles that examined session Rating of Perceived Exertion (sRPE), 3 utilized TSB, 9,10,24 while the other 15 implemented ACWR. 17,20,21,23,25,26,28,30,32,33,[35][36][37][38]40 Of the three studies that utilized TSB, 9,10,24 one did not find any clear effects on injury risk after comparing a two standard deviation increase to the mean, 24 while another found no relationship between a TSB >200% and injury risk in the current week when compared to a TSB of 100%. 10 It was found, however, that a TSB >100% increased injury risk in the next week compared to a TSB of 100% (RR=2.20). ...
... 21 This makes further systematic analysis of the effectiveness of the ACWR while using ILs difficult. However, with the "good" quality of included studies, 9,10,20,21,[24][25][26][28][29][30]37,38 it does appear that utilizing ACWR and sRPE may relate to injury risk in athletic populations. In addition, it was also found that utilizing ACWR and measures of stress, sleep, mood, and energy were significantly related to injury risk. ...
Article
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Purpose: Low injury rates have previously been correlated with sporting team success, highlighting the importance of injury prevention programs. Recent methods, such as acute:chronic workload ratios (ACWR) have been developed in an attempt to predict and manage injury risk; however, the relation between these methods and injury risk is unclear. The aim of this systematic review was to identify and synthesize the key findings of studies that have investigated the relationship between ACWR and injury risk. Methods: Included studies were critically appraised using the Downs and Black checklist, and a level of evidence was determined. Relevant data were extracted, tabulated, and synthesized. Results: Twenty-seven studies were included for review and ranged in percentage quality scores from 48.2% to 64.3%. Almost perfect interrater agreement (κ = 0.885) existed between raters. This review found a high variability between studies with different variables studied (total distance versus high speed running), as well as differences between ratios analyzed (1.50-1.80 versus ≥1.50), and reference groups (a reference group of 0.80-1.20 versus ≤0.85). Conclusion: Considering the high variability, it appears that utilizing ACWR for external (eg, total distance) and internal (eg, heart rate) loads may be related to injury risk. Calculating ACWR using exponentially weighted moving averages may potentially result in a more sensitive measure. There also appears to be a trend towards the ratios of 0.80-1.30 demonstrating the lowest risk of injury. However, there may be issues with the ACWR method that must be addressed before it is confidently used to mitigate injury risk. Utilizing standardized approaches will allow for more objective conclusions to be drawn across multiple populations.
... The sports included Australian Football (n = 7, 31.8%) [26,[32][33][34][35][36][37], soccer (n = 6, 27.3%) [23,[38][39][40][41][42], Rugby League (n = 2, 9.1%) [43,44], Rugby Union (n = 1, 4.5%) [45], Gaelic Football (n = 1, 4.5%) [46], hurling (n = 1, 4.5%) [47], cricket (n = 1, 4.5%) [22], American Football (n = 1, 4.5%) [48], volleyball (n = 1, 4.5%) [49] and basketball (n = 1, 4.5%) [50]. Sample size ranged from 13 to 173 and all studies were conducted on male participants only. ...
... The mean age range was 22.0-26.7 years with two studies not reporting age [38,45]. The level of the participants was reported as elite (n = 16, 72.7%) [22, 23, 26, 33-39, 41-44, 46, 49], professional (n = 4, 18.2%) [32,40,45,50], amateur (n = 1, 4.5%) [47] and collegiate (n = 1, 4.5%) [48]. Ten studies (45.5%) [23,36,38,42,[44][45][46][48][49][50] monitored participants across 1 season, eight studies (36.4%) [26,32,34,35,40,43,44,47] across 2 seasons, one study (4.5%) [39] across 3 seasons, two studies (9.1%) [33,37] across 4 seasons and one study (4.5%) [22] across 5 seasons. ...
... The level of the participants was reported as elite (n = 16, 72.7%) [22, 23, 26, 33-39, 41-44, 46, 49], professional (n = 4, 18.2%) [32,40,45,50], amateur (n = 1, 4.5%) [47] and collegiate (n = 1, 4.5%) [48]. Ten studies (45.5%) [23,36,38,42,[44][45][46][48][49][50] monitored participants across 1 season, eight studies (36.4%) [26,32,34,35,40,43,44,47] across 2 seasons, one study (4.5%) [39] across 3 seasons, two studies (9.1%) [33,37] across 4 seasons and one study (4.5%) [22] across 5 seasons. ...
Article
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Background There has been a recent increase in research examining training load as a method of mitigating injury risk due to its known detrimental effects on player welfare and team performance. The acute:chronic workload ratio (ACWR) takes into account the current training load (acute) and the training load that an athlete has been prepared for (chronic). The ACWR can be calculated using; (1) the rolling average model (RA) and (2) the exponentially weighted moving average model (EWMA). Objective The primary aim of this systematic review was to investigate the literature examining the association between the occurrence of injury and the ACWR and to investigate if sufficient evidence exists to determine the best method of application of the ACWR in team sports. Methods Studies were identified through a comprehensive search of the following databases: EMBASE, Medline, SPORTDiscus, SCOPUS, AMED and CINAHL. Extensive data extraction was performed. The methodological quality of the included studies was assessed according to the Newcastle–Ottawa Scale (NOS) for Cohort Studies. Results A total of 22 articles met the inclusion criteria. The assessment of article quality had an overall median NOS score of 8 (range 5–9). The findings of this review support the association between the ACWR and non-contact injuries and its use as a valuable tool for monitoring training load as part of a larger scale multifaceted monitoring system that includes other proven methods. There is support for both models, but the EWMA is the more suitable measure, in part due to its greater sensitivity. The most appropriate acute and chronic time periods, and training load variables, may be dependent on the specific sport and its structure. Conclusions For practitioners, it is the important to understand the intricacies of the ACWR before deciding the best method of calculation. Future research needs to focus on the more sensitive EWMA model, for both sexes, across a larger range of sports and time frames and also combinations with other injury risk factors.
... Specific to collegiate soccer, understanding the underpinning physiological components for athletic competition is an important practice of collegiate strength and conditioning coaches. 9,10 However, what is less known is if these measures correlate with on-field performance, or if they are a requisite for onfield success. As such, recent investigations in men's basketball, 9,11 women's soccer 10 and across men's and women's collegiate athletic programs 12 have begun to examine if physiological fitness relates to on-field performance. ...
... 9,10 However, what is less known is if these measures correlate with on-field performance, or if they are a requisite for onfield success. As such, recent investigations in men's basketball, 9,11 women's soccer 10 and across men's and women's collegiate athletic programs 12 have begun to examine if physiological fitness relates to on-field performance. One way to determine on-field performance and success may be to investigate playing time. ...
... 13,16 Countermovement jump (CMJ): The CMJ was used as a measure of lower-body power and was performed on an electronic jump switch mat (Just Jump, Pro Botics Inc, Hunstville, AL, USA). 9,10,17,18 Participants began in a full standing position with feet placed no wider than shoulder-width and arms at the side. Participants then performed a countermovement with the arms swinging backward, occurring in a self-selected manner. ...
Article
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Cite this article Stone BL, Minson KL, Anderson EC, Lockie RG, Dawes JJ. The relationship between pre-season testing performance and playing time among NCAA Division II men's soccer athletes over a competitive season: A pilot analysis. Sport Exerc Med Open J. 2019; 5(2): 30-35. ABSTRACT Copyright 2019 by Dawes JJ. This is an open-access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which allows to copy, redistribute, remix, transform, and reproduce in any medium or format, even commercially, provided the original work is properly cited. 30 Orginal Research | Volume 5 | Number 2| cc Aim The purpose of this study was to investigate the relationships between pre-season testing performance and playing time within a Division II men's soccer team over a competitive season. Methods Data was collected from pre-season athletic performance testing data for 13 male National Collegiate Athletic Association (NCAA) Division II men's soccer players (age=20±1.5-years; height=180±6 cm; weight=75±7 kg), and was analyzed to determine if relationships existed between physical performance tests (countermovement jump height, peak anaerobic power in watts derived from jump height], change-of-direction performance (505-agility, modified T-test), linear speed (10 m and 30 m sprint intervals), and aerobic fitness (20 m multi-stage fitness test), and playing time over a collegiate season were provided by the University's coaching staff and retrospectively analyzed. Results A Pearson's moment correlations correlation revealed significant (p<0.05) moderate relationships between playing time and 10 m speed (r=-0.569) only. Discussion These results suggest that linear speed, in particular acceleration over short distance, could be a key characteristic that has some influence on playing time for Division II men's soccer players. Conclusion Pre-season testing of soccer players is commonly used to assess athletic potential. Minimal research has investigated the associations between these tests and playing time over the course of a collegiate season.
... There is almost no published guidance available for conservative rehabilitation of football athletes following posterior hip dislocation. However, the authors were relatively confident that this subject was appropriately progressed using an algorithmic approach to loading based upon preinjury 1RM's with observance of consensus safe acute to chronic workload ratios [18][19][20] for PRE and impact activity, in conjunction with consideration of expected tissue healing time and monitoring of subject symptom irritability. A case presented by Yates et al 5 included a five-month rehabilitation progression initiated with primarily non-weightbearing progressive resistance exercise (PRE) followed by pool activity to protected squat and leg press training in the second month. ...
... Loading of the left hip and lower extremity was carefully controlled throughout the course of care utilizing rating of perceived exertion,16,17 percentage of pre-injury 1-repetition-maximum (1RM), and a rolling 1.1-1.2 acute to chronic workload ratio[18][19][20] (ACWR) for prescribed resistance exercise, return to run, and plyometric activities. ...
Article
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Background and purpose: American football generates the most sports-related injuries in the United States, with tackling as the leading injury mechanism. Overall injury rate at the collegiate level has been reported as 8.61 per 1,000 athlete exposures (AEs) - twice the rate of high school levels; competition injury rates are reported as high as 36.94/1000 AEs. Traumatic hip dislocation is an uncommon injury typically arising from high-energy axial impact with only 2-5.5% occurring during sports activities. Case description: A 22-year-old NCAA Division I football defensive back who experienced extreme left hip pain following contact with another player with his hip flexed during a game was diagnosed with a type 1 posterior hip dislocation, a grade 1 medial collateral ligament sprain with concomitant posterior thigh and hip muscle strains. Key impairments were limited left lower extremity motor performance, range of motion deficits, left hip pain, and diminished function and weight-bearing ability. Outcomes: The athlete reintegrated into typical defensive back off-season training approximately four to five months post injury without restrictions, however presented with new anterior hip pain seven months post injury revealing occult sequelae requiring surgical intervention. He was able to return to full play the following football season. Discussion: This case report describes the successful return to sport of a Division I football player who sustained a traumatic posterior hip dislocation and complicated course including surgical intervention secondary to associated sequelae. Level of evidence: 5.
... The use of an adequate periodization is crucial to maximise athletes' performance (Nunes et al., 2014;Pliauga et al., 2018) and minimize the risk of non-functional overreaching, overtraining and injuries (Weiss et al., 2017). In basketball, training periodization involves periods of high physical loads followed by taper periods (Nunes et al., 2014;Pliauga et al., 2018). ...
... Additionally, internal training load was subjectively assessed using sRPE method (sRPE-TL), which was extensively used in basketball. Paulauskas et al., 2019;Weiss et al., 2017). Each player was required to provide a global intensity score using the category ratio scale (CR-10 Borg's scale) (Borg, 1998) approximately 30 min after each training session or friendly game answering to the question: "How intensive was your training session/ game?" (Foster et al., 2001). ...
Article
This study aimed to investigate between- and within-team changes in workload [PlayerLoad (PL), training impulse (TRIMP) and session rate of perceived exertion training load (sRPE-TL)], readiness [heart rate variability (HRV)], and physical performance [20-m sprint test (including 10-m split time), countermovement jump (CMJ) and yo-yo intermittent recovery test level 1 (YYIR1)] during 3-week intensified preparation periods in female, national Under18 (n = 12, age = 18.0 ± 0.5y, stature = 180.4 ± 7.5 cm, body mass = 72.7 ± 9.3 kg) and Under20 (n = 12, age = 19.6 ± 0.8y, stature = 178.6 ± 6.4 cm, body mass = 68.0 ± 5.9 kg) basketball teams. Under18 team revealed small-to-moderate statistically significantly higher values in workload [PL: p = 0.010; ES = Small; TRIMP: p = 0.004; ES = Moderate; sRPE-TL: p < 0.001; ES = Moderate] and moderately lower readiness values (p = 0.023; ES = Moderate) compared to Under20. Within-team analysis showed no differences in workload in Under20 and statistically significant reduction (p < 0.05) in Week3 (taper period) in Under18. Pre- and post-preparation changes showed Under18 increasing only YYIR1 performance (p < 0.001; ES = Very large). Differently, Under20 statistically improved in 10-m split time (p = 0.003; ES = Moderate), CMJ (p = 0.025; ES = Moderate) and YYIR1 (p < 0.001; ES = Large). A constant adequate workload positively benefits players’ readiness and physical performances during short intensified preparation periods. Conversely, using high workload with periodization strategies encompassing short overload and taper phases induced positive changes on players’ aerobic performance, lower readiness values and no changes in anaerobic performances.
... Player monitoring in basketball can be implemented to quantify a range of variables such as training and competition loads (Fox, Scanlan, & Stanton, 2017), recovery status (Edwards et al., 2018), and wellness (Edwards et al., 2018). These parameters are important to monitor as they are closely aligned with performance-related outcomes such as fitness (Fox et al., 2017), fatigue (Edwards et al., 2018), and injury risk (Weiss, Allen, McGuigan, & Whattman, 2017). Player monitoring in basketball can provide large datasets to inform player management strategies. ...
... Player monitoring in basketball can provide large datasets to inform player management strategies. However, quantifying workloads is the primary application of player monitoring in basketball (Fox et al., 2017;Weiss et al., 2017;Schelling & Torres, 2016;Fox, Stanton, & Scanlan, 2018). Workload monitoring in basketball involves quantifying external and internal load. ...
Article
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The purpose of this study was to examine player monitoring approaches used by basketball practitioners with a specific focus on the use of microsensors. An online survey was disseminated to basketball practitioners via international basketball-related organisations and social media channels. Multiple response, Likert-scale level of agreement, and open-ended questions captured data regarding if, and how player monitoring was performed, as well as barriers and facilitators to player monitoring, with an emphasis on the use of microsensors. Forty-four basketball practitioners completed the survey. Twenty-seven respondents (61%) implement player monitoring and thirteen (30%) use microsensors. Despite implementing player monitoring, over 85% of practitioners modify training based on their own observation. Respondents not currently monitoring players (39%) would commence monitoring if the tools or equipment were provided. 74% of respondents agree that microsensors are expensive. Only 56% of practitioners who use microsensors feel they have support for using the technology and analysing/interpreting the data. These findings suggest a low uptake of microsensors for player monitoring in basketball. Coaches and practitioners perceive player monitoring approaches to be cost-prohibitive and appear unsure of how player monitoring data should be used to optimise training outcomes for players.
... The assessment of weekly fluctuations in workloads is funda mental to prevent the occurrence of noncontact injuries in basket ball. [8][9] Indeed, spikes in TL >15% might increase the risk of noncontact injuries in team sports.9 Moreover, the acutexhronic workload ratio (ACWR), which represents the weekly workload performed by athletes (acute load) divided by the workload they were prepared for across the previous 4 weeks (chronic load), has been identified as an index of athlete preparedness and valuable monitoring variable in basketball. ...
... Moreover, the acutexhronic workload ratio (ACWR), which represents the weekly workload performed by athletes (acute load) divided by the workload they were prepared for across the previous 4 weeks (chronic load), has been identified as an index of athlete preparedness and valuable monitoring variable in basketball. 8 Weiss et al8 demonstrated an ACWR ranging between 1 and 1.5 might be optimal to prevent overuse injuries in male basketball players. Therefore, monitoring the weekly fluctuations in TL seems essential to avoid undue spikes in workload and subsequently minimize the risk of nonfunctional overreaching and noncontact injuries occurring.7-8 ...
Article
Purpose: To assess the weekly fluctuations in workload and differences in workload according to playing time in elite female basketball. Methods: Twenty-nine female basketball players (mean ± standard deviation, age: 21±5yr; stature: 181±7cm; body mass: 71±7kg; and playing experience: 12±5yr) belonging to the 7 female basketball teams competing in the first division Lithuanian Women's Basketball League (LMKL) were recruited. Individualized training loads (TL) and game loads (GL) were assessed using the session-RPE following each training session and game during the entire in-season phase (24 weeks). Percentage (%) changes in total weekly TL (weekly TL+GL), weekly TL, weekly GL, chronic workload, acute:chronic workload ratio (ACWR), training monotony, and training strain were calculated. Mixed linear models were used to assess differences for each dependent variable, with playing time (low vs high) used as fixed factor and subject, week, and team as random factors. Results: The highest changes in total weekly TL, weekly TL, and ACWR were evident in week 13 (47%, 120%, and 49% respectively). Chronic workload showed weekly changes ≤10%, while monotony and training strain registered highest fluctuations in weeks 17 (34%) and 15 (59%), respectively. A statistically significant difference in GL was evident between players completing low and high playing times (p=0.026, moderate), while no significant differences (p>0.05) were found for all other dependent variables. Conclusions: Coaches of elite female basketball teams should monitor weekly changes in workload during the in-season phase to identify weeks that may predispose players to unwanted spikes and adjust player workload according to playing time.
... Recently, interest has grown about the acute:chronic workload ratio (ACWR), which is calculated as the ratio between the TL performed in the current week (acute, representing fatigue status) and that performed in the past 3-6 weeks (chronic, representing fitness status) (18). The ACWR ratio calculated using RPE-based TL has been shown to predict injuries in different sports (18), including basketball (44). ...
... Weekly TL (w-TL) was calculated in AUs as the sum of the s-TL performed by the team in the week (e.g., Tuesday: 450 AU, Wednesday: 500 AU, Friday: 430 AU, game: 240 AU = 1,520 AU); weekly training TL (w t -TL) as the sum of the AUs of the 3 training session, excluding the game load. Acute:chronic workload ratio was calculated starting from week 4, by dividing the current week w-TL (acute) by the rolling average of the w-TL for the previous 4 weeks (chronic), as suggested by previous studies (18,44). For example, if the acute w-TL is 1,500 AU, and the chronic w-TL is 1,250 AU, the ACWR is 1.2. ...
Article
This study investigated the relationship between internal training load and perceived recovery of semi-professional female basketball players during the competitive season. Eleven female players were monitored for 14 weeks during the in-season phase. For each event (training and game), data were collected as follows: (a) Total Quality Recovery (TQR) score before the event (TQRpre); (b) session Rating of Perceived Exertion (sRPE) 20 minutes after completion of the event, to calculate training load (s-TL) of the event; and (c) TQR scores 12 (TQRpost12) and 24 hours (TQRpost24) after the event. Data were analyzed for daily, weekly, and meso- cycle (regular season; regional play-off; and national play-off) time frames. Daily analysis showed that: TQRpost12 was lower than TQRpre (p= 0.001) and TQRpost24 (p= 0.001); s-TL had a moderate negative correlation with TQRpost12 (r = 0.48, p = 0.002); the difference between TQRpre and RPE (TS-D) had a very large positive correlation with TQRpost12 (r = 0.70, p , 0.001); and TQRpost24 was not significantly correlated with training parameters. Weekly analysis highlighted a very large negative correlation between the acute:chronic workload ratio and TQR collected at the start of the following week (TQRfw) (r = 0.86, p= 0.001). Finally, although weekly TL and TQRfw did not differ between mesocycles, their correlation increased in the later seasonal phases. This study demonstrated negative relationships between training load and recovery of semi-professional female basketball players at daily, weekly, and mesocycle levels. Therefore, concurrently monitoring training and recovery with the sRPE method and TQR scale is recommended for designing training schedules in basketball.
... More specifically, professional teams tend to accumulate between 2 and 3 matches per week during the season (Fox et al., 2020;Yang et al., 2021), meaning that players who accumulate significant playing time could struggle to reach optimal recovery after competition (Dellal et al., 2015;Calleja-González et al., 2016;Crowther et al., 2017). Thus, a detailed understanding of basketball players' recovery during congested and non-congested schedules is critical to enhanced training prescription when the aim is to optimise the player's in-game performance and health (Stojanovic and Ostojic, 2012;Weiss et al., 2017;Sansone et al., 2020). ...
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The purpose of the study was to describe and compare recovery status after official basketball competition in players who underwent NESA neuromodulation treatment (NNT) in weeks with one or two matches. The recovery parameters of 12 professional male basketball players (mean ± SD, age: 20.6 ± 2.7 yr; height: 197.8 ± 11.7 cm; and body mass: 89.0 ± 21.2 kg) that competed in the LEB Plata (Spanish third division) were monitored 2 days after match-play over 6 weeks, and included: 1) the Hooper Test, which combines four subjective variables (sleep, stress, fatigue and soreness); 2) common biochemical markers (e.g., testosterone, cortisol and ratio T:C); and 3) lowest heart rate [HR], average HR, HR variability, sleep duration, awake time during night and onset latency before asleep). Players that completed NNT presented differences compared to the control group in sleep data. For instance, the lowest HR ( p < 0.001), average HR ( p < 0.001) and total awake time ( p = 0.04) were significantly reduced in the NNT group. On the contrary, the control group presented greater values than the NNT group in the subjective Hooper Test, although only stress presented significant differences (Control 2.5 ± 1.2 vs. NNT cost or 3.2 ± 0.9; p = 0.01). Additionally, there were no significant differences in recovery parameters between weeks with one or two matches. In conclusion, the results suggest that players that underwent NNT tended to improve their sleep quality. Nevertheless, player’s values in the biochemical markers and wellness status remained similar in both groups. The fact that no significant differences were found between weeks with one or two matches could help basketball professionals to determine that a congested schedule does not seem to negatively alter recovery status. Clinical Trial Registration: https://clinicaltrials.gov/ct2/show/NCT04939181?term=NCT04939181 , NCT04939181
... Training sessions may include shooting drills, often controlled, non-contact and low intensity, considerably different than the fast, explosive, unpredictable nature of game play. Describing the risk and aetiology of injuries arising from games and training separately may influence the adoption of injury prevention strategies such as neuromuscular training programmes (Bonato, Benis, & La Torre, 2018;Owoeye, Palacios-Derflingher, & Emery, 2018), load management strategies (Weiss, Allen, McGuigan, & Whatman, 2017) or protective equipment (McGuine, Brooks, & Hetzel, 2011). ...
Article
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Objectives Describe the injury risk of English youth basketball, comparing game versus training injury incidence and burden. Design 5 season (2013/14-2018/19) prospective cohort study. Setting Basketball academy at an English sports college. Participants Male basketball players (n = 110, mean age; 17.3 ± 0.9 years). Main outcomes measures Descriptive data regarding game and training injury incidence (injuries per 1000 athlete-exposures (AE)) and burden (severity x incidence) are provided with 95% confidence intervals (CI). Rate ratios (RR; 95% CI) were used to compare outcome measures, with results statistically significant if the 95% CI did not pass 1.0. Results Fifty-four injuries were sustained during 13,350-AE (1666 games, 9684 training). Game injury incidence (12.0/1000-AE, 95% CI 6.7–17.3) was significantly greater than training injury incidence (2.4/1000-AE, 95% CI 1.4–3.3; RR = 5.1, 95% CI 2.8–9.2). Games had a significantly greater injury burden (216 days absence/1000-AE, 95% CI 121-311) than training (62 days absence/1000-AE, 95% CI 37-88; RR = 3.5, 95% CI 1.9–6.3). The ankle was the most injured body location (37%), whilst over 50% of injuries occurring through contact mechanisms. Conclusion This study is the most comprehensive description of injury epidemiology in English youth basketball to date. This information can inform evidence-based injury prevention strategies to mitigate risk in this population.
... Concretely, basketball has become one of the most popular team sports, with a large economic interest, revealed in the great number of games that top-level team play each season. These demands need to prepare players to perform optimally physically, technically, and cognitively (Folgado et al., 2018;Rico-González et al., 2020), minimizing fatigue and risks of injury (Stojanovic & Ostojic, 2012;Weiss et al., 2017). Therefore, monitoring the players' game and training demands is a crucial task in optimizing basketball performance García, Schelling, et al., 2021;Vázquez-Guerrero & Garcia, 2020) . ...
Article
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The purpose of the study was to examine the relationships of different training load variables and wellness responses, with in-game basketball performance across playing positions (backcourt and frontcourt players). External load variables (e.g., total distance, accelerations, etc), internal responses (e.g., rate of perceived exertion [RPE]) and wellness status (e.g., Hooper index) were monitored during 7 consecutive in-season months on 15 professional male basketball players. Besides, game-related individual statistics (performance index rating [PIR], and player total contribution [PTC]) were used to assess the performance during competition. Although no positive relationship was found between training load variables and basketball game performance, some wellness questionnaire parameters were significantly associated to game-related individual statistics. In particular, we found that only competing against direct rivals, players that reported stable values of stress stability achieved significantly higher PTC and PIR scores than players with high variability in stress values (8.53 IQR [6.09, 14.8] vs 0.00 IQR [-0.46, 0.84], and 0.47 ± 0.40 vs 0.10 ± 0.50; respectively; P < 0.05). Similarly, players with variable values of stress managed to maximize PIR scores during losses compared to players that presented high variability in stress levels (0.42 IQR [0.27, 0.55] vs 0.00 IQR [-0.12, 0.37], P < 0.05). Regarding playing positions, backcourt players showed higher PTC scores compared to frontcourt players when the fatigue levels are stable during the microcycle (8.27 ± 5.75 vs 4.77 ± 4.42; P < 0.05). Because basketball teams tend to accumulate more backcourt players that frontcourt players, it would be advisable to control training load maintaining it stable and avoiding load spikes during microcycle to allow team performance optimization. In conclusion, the results suggest that the best performances during official competition are not associated with higher training external and internal loads. Nevertheless, the wellness status could provide useful information when assessing player’s training responses and approaching possible peak performance during basketball competition. This shows the necessity to assess basketball performance from a holistic approach and consider more than just physical and physiological parameters, such as decision-making and psychological capacities, to better understand player’s performance during basketball competition.
... Previous studies have confirmed that PL is suitable for monitoring load in ball sports [16,17,31]. The finding of our study also supports this monitoring method. ...
Article
Full-text available
This study quantified the strength of the relationship between the percentage of heart rate reserve (%HRR) and two acceleration-based intensity metrics (AIMs) at three sensor-positions during three sport types (running, basketball, and badminton) under three intensity conditions (locomotion speeds). Fourteen participants (age: 24.9 ± 2.4 years) wore a chest strap HR monitor and placed three accelerometers at the left wrist (non-dominant), trunk, and right shank, respectively. The %HRR and two different AIMs (Player Load per minute [PL/min] and mean amplitude deviation [MAD]) during exercise were calculated. During running, both AIMs at the shank and PL at the wrist had strong correlations (r = 0.777–0.778) with %HRR; while other combinations were negligible to moderate (r = 0.065–0.451). For basketball, both AIMs at the shank had stronger correlations (r = 0.604–0.628) with %HRR than at wrist (r = 0.536–0.603) and trunk (r = 0.403–0.463) with %HRR. During badminton exercise, both AIMs at shank had stronger correlations (r = 0.782–0.793) with %HRR than those at wrist (r = 0.587–0.621) and MAD at trunk (r = 0.608) and trunk (r = 0.314). Wearing the sensor on the shank is an ideal position for both AIMs to monitor external intensity in running, basketball, and badminton, while the wrist and using PL-derived AIM seems to be the second ideal combination.
... Athletes were asked to rate their session RPE within 15-30 min after the end of each game. [24][25][26][27] Data processing Openfield TM was used to process positional and IMA data. It can be used to analyze micromovements, regardless of unit orientation and positional data. ...
Article
Purpose 3 × 3 is a variation of classic basketball (BB) that imposes new demands on both athletes and coaches. The objective of this study was to comprehensively describe similarities and differences in load structures between 3 × 3 and BB. Method Between January 2020 and July 2021, internal and external load parameters of 90 elite BB and 3 × 3 athletes were monitored during 84 games using a local positioning system (10 Hz) and inertial movement sensors (100 Hz). Results Male BB games last about 90 min; female games are shorter (82 min). Game duration in 3 × 3 is about 16 min. Compared with BB, 3 × 3 athletes perform more medium- and high-intensity accelerations and decelerations, explosive efforts, jumps, and changes of direction (CODs) per min. Average heart rates do not differ between the two disciplines, yet 3 × 3 games are rated as more exhausting than BB games. During a 3 × 3 tournament, no decrease in load characteristics is found in the final games. However, ratings of perceived exertion increase consecutively. Average possession duration in 3 × 3 is 5.4 s in which 1.4 passes are played. Most 2-point shots and turnovers occur in the last minutes (11 min – end) of the game. Conclusion The findings of this study will help 3 × 3 coaches and athletes to design more effective training programmes and improve athletic performance. They indicate a need to focus on high accelerations, transitions, COD skills, and shooting under pressure in 3 × 3.
... Findings reveal that injury incidence is in fact associated with spikes in the ACWR in these sports as well, with a combination of external and internal workload data appearing to have the most predictive power. [4][5][6][7] When differing acute and chronic time windows were implemented to determine which best explains injury likelihood, it was found that a 3:21 days ratio was more predictive of injury than the commonly used 7:28 days ratio for Australian footballers, suggesting that the best ratio to use might be sport-specific. 5 Furthermore, the method for calculating ACWR has been hotly contested. ...
Article
Predicting sports injuries is a complex phenomenon given the multitude of risk factors involved and the need for an inciting event. Recent evidence suggests that the acute:chronic workload ratio (ACWR) is a potentially useful tool for quantifying athlete workloads, with athletes at increased risk of injury when the ACWR is higher relative to a lower ACWR. While several team sports have been studied in the ACWR literature, there is a paucity of studies that focus on volleyball athletes, and no studies that use knee pain as an outcome. Furthermore, controversy exists as inconsistent results among studies may be attributed to differences in calculating the ACWR. Our objective was to assess different definitions of the ACWR for predicting knee pain in elite volleyball athletes. We expected to see agreement with the literature in that ACWR would be positively associated with knee pain. We conducted a retrospective, exploratory analysis on a data set from a University varsity volleyball team. Our mixed effect modelling indicated that the coefficient estimates for the ACWR variants were small and statistically insignificant. The variant used did not have a major influence on the relationship with knee pain score, and the strength of the relationship was weak.
... These common injuries are brought to the public's attention when they happen to high profile players in the National Basketball Association (NBA). Numerous studies have demonstrated positive prevention strategies for common basketball injuries [3][4][5][6][7]. Accurate sports injury information and reliable rehabilitation data are pertinent when disseminating health information to the general public. ...
Article
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Context Injuries are common among high profile players in the National Basketball Association (NBA), and could provide an opportunity for physicians to provide accurate sports injury information and reliable rehabilitation data to the general public in the immediate aftermath. Objectives To evaluate social media trends to investigate public interest in athletic injuries in the NBA and to evaluate the length of maintained interest in these injuries. Methods The Google Trends tool was used to analyze search data around two high profile players—Kevin Durant and Klay Thompson—who suffered injuries during the 2019 NBA Finals. The results were compared to the expected search forecast derived from an autoregressive integrated moving algorithm model. Results Both players were associated with a mean increase of 1,052.4% (standard deviation [SD], 703.96%) in relative search volumes for terms related to their injuries. This data showed a significant increase in search engine activity related to injuries associated with NBA players in the first 6.13 days (SD, 3.14 days) following the injuries, marking a substantial timeframe for public engagement. Conclusions Search traffic information may be beneficial to the sports medicine community, as social media can provide a platform for patient education in a limited timeframe. By increasing patient awareness and knowledge regarding athletic injuries, social media can expand the pool of potential patients for physicians and surgeons.
... The concept of an ACWR "sweet spot", whereby injury risk is highest when you have a low ( < 0.8) or high ( > 1.3) ACWR value, was first introduced in 2016 [1]. Since then, several others have reported sweet spots, however with differing sweet spot values in each case [38,42,43], with subsequent questions as to whether demonstration of a sweet spot is a robust concept or a methodological artefact. In this study, of the 64 different calculation methods assessed, only 15 (23 %) showed a significant ACWR "sweet spot" for all injury risk and 18 (28 %) for soft tissue injuries. ...
Article
Training load monitoring has grown in recent years with the acute:chronic workload ratio (ACWR) widely used to aggregate data to inform decision-making on injury risk. Several methods have been described to calculate the ACWR and numerous methodological issues have been raised. Therefore, this study examined the relationship between the ACWR and injury in a sample of 696 players from 13 professional rugby clubs over two seasons for 1718 injuries of all types and a further analysis of 383 soft tissue injuries specifically. Of the 192 comparisons undertaken for both injury groups, 40 % (all injury) and 31 % (soft tissue injury) were significant. Furthermore, there appeared to be no calculation method that consistently demonstrated a relationship with injury. Some calculation methods supported previous work for a “sweet spot” in injury risk, while a substantial number of methods displayed no such relationship. This study is the largest to date to have investigated the relationship between the ACWR and injury risk and demonstrates that there appears to be no consistent association between the two. This suggests that alternative methods of training load aggregation may provide more useful information, but these should be considered in the wider context of other established risk factors.
... This finding contrasts with the originally described ACWR concept, which suggested an increased injury risk when the ACWR was low. 24 We note that few (n = 2 62,68 ) other studies have supported this aspect of the concept, and question its biological rationale. ...
Article
Objectives: To investigate whether the relationship between the acute:chronic workload ratio (ACWR) and health problems varies when different methodological approaches are used to quantify the relationship. Design: Prospective cohort study. Methods: An online questionnaire was used to collect daily health and training information from 86 elite youth footballers for 105 days. The relationship between players' training load and health was analysed using a range of different definitions of ACWR and health problems. We used a 21-day and 28-day chronic period, coupled and uncoupled calculations, and exponentially weighted moving average (EWMA) and rolling average (RA). ACWR data were categorized into three bins (low, medium and high) using pre-defined categories and z-scores, and we compared medium to high, medium to low and low to high categories. The outcome was defined in three ways: "all health problems", "all injuries", and "new non-contact injuries". We performed random-effects logistic regression analyses of all combinations, a total of 108 analyses. Results: We recorded 6250 training days and 196 health problems. Of the 108 analyses performed, 24 (22%) identified a statistically significant (p<0.05) association between ACWR and health problems. A greater proportion of significant associations were identified when using an EWMA (42% of analyses), when comparing low and high categories (33%), and using "all health problems" definition (33%). Conclusions: The relationship between ACWR and health problems was dependent on methodological approach. J Orthop Sports Phys Ther, Epub 20 Jan 2021. doi:10.2519/jospt.2021.9893.
... defense), and injury history are currently reported to be associated with injury outcomes [8,10,11]. Based on best available evidence, primary targets for injury prevention in basketball are exercise-based neuromuscular training warm-up programs [11][12][13][14][15][16], workload mediation [17][18][19][20][21] and equipment strategies (e.g., mouthguards) [22,23]. Screening as a "first layer" prediction tool for injury prevention (to determine who should have prevention intervention-all players should) in team sports has been recently discouraged in scientific discussions considering the multifaceted nature of sports injury [24]. ...
Chapter
Mitigating the risk of injury in basketball is essential for continued player participation, performance, team success, and future health. Considering the multidimensionality of the etiology of sports injuries, a complex systems approach is crucial to unravel their emergence. While screening may not be an optimal “first layer” prediction tool for injury prevention in team sports (all athletes deserve preventive intervention), it may be a valuable “second layer” monitoring tool for effective risk management in individual players, using a complex predictive model. This review describes the characteristics of a complex system and provides a rationale for its application in basketball injury etiology and prevention. Next, a review of current theories for sports injury etiology and injury prevention is presented and a novel Complex Model for the Etiology of Basketball Injury is proposed. Finally, potential applications of the proposed model in basketball injury prevention research and practice are discussed, including its potential application as a risk stratification tool for an additional layer of injury risk mitigation in individual players.
... Presumably, the low chronic loads leave players underprepared for the high physical demands associated with competition. These findings of low chronic loads and spikes in load increasing injury risk have subsequently been replicated by over 20 different research groups from a wide range of sports, including basketball [11,12]. The NBA season spans 82 games; basketball players need to be well-conditioned to tolerate the running, jumping, and change of direction loads required during the season. ...
Chapter
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“Load Management” has become a common term used in basketball, particularly in the National Basketball Association (NBA). While the media and spectators may interpret load management as removing players from competition (or training) and providing them with more rest, the reality is that when applied appropriately, well-established training principles provide players with the opportunity to perform at a high level, more often. This chapter discusses the concept of “load” and the capacity of athletes to tolerate load. Importantly, factors other than load that impact on load tolerance (e.g. sleep, travel and psychological stress) are discussed. The latest evidence surrounding training load, injury and performance is summarised and practical examples of how to interpret athlete management data are provided.
... changing the questionnaire prompt/verbal anchors/duration used) and either not providing any reasoning for the change or justifying the change by citing older studies. For example, Weiss et al. [119] cited Foster's 2001 study to justify their use of sRPE in basketball, but then further described their sRPE methodology by citing Coutts et al.'s 2007 work with triathletes [120] and Impellizzeri et al.'s work with soccer [121], both of which cite Foster's 1995 paper for their methods. Another study by Doeven et al. [16] describes that sRPE is a valid method in elite basketball, but uses a 6-20 point RPE scale with no prior validation in the basketball literature. ...
Article
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Background Measuring the physical work and resultant acute psychobiological responses of basketball can help to better understand and inform physical preparation models and improve overall athlete health and performance. Recent advancements in training load monitoring solutions have coincided with increases in the literature describing the physical demands of basketball, but there are currently no reviews that summarize all the available basketball research. Additionally, a thorough appraisal of the load monitoring methodologies and measures used in basketball is lacking in the current literature. This type of critical analysis would allow for consistent comparison between studies to better understand physical demands across the sport. Objectives The objective of this systematic review was to assess and critically evaluate the methods and technologies used for monitoring physical demands in competitive basketball athletes. We used the term ‘training load’ to encompass the physical demands of both training and game activities, with the latter assumed to provide a training stimulus as well. This review aimed to critique methodological inconsistencies, establish operational definitions specific to the sport, and make recommendations for basketball training load monitoring practice and reporting within the literature. Methods A systematic review of the literature was performed using EBSCO, PubMed, SCOPUS, and Web of Science to identify studies through March 2020. Electronic databases were searched using terms related to basketball and training load. Records were included if they used a competitive basketball population and incorporated a measure of training load. This systematic review was registered with the International Prospective Register of Systematic Reviews (PROSPERO Registration # CRD42019123603), and approved under the National Basketball Association (NBA) Health Related Research Policy. Results Electronic and manual searches identified 122 papers that met the inclusion criteria. These studies reported the physical demands of basketball during training (n = 56), competition (n = 36), and both training and competition (n = 30). Physical demands were quantified with a measure of internal training load (n = 52), external training load (n = 29), or both internal and external measures (n = 41). These studies examined males (n = 76), females (n = 34), both male and female (n = 9), and a combination of youth (i.e. under 18 years, n = 37), adults (i.e. 18 years or older, n = 77), and both adults and youth (n = 4). Inconsistencies related to the reporting of competition level, methodology for recording duration, participant inclusion criteria, and validity of measurement systems were identified as key factors relating to the reporting of physical demands in basketball and summarized for each study. Conclusions This review comprehensively evaluated the current body of literature related to training load monitoring in basketball. Within this literature, there is a clear lack of alignment in applied practices and methodological framework, and with only small data sets and short study periods available at this time, it is not possible to draw definitive conclusions about the true physical demands of basketball. A detailed understanding of modern technologies in basketball is also lacking, and we provide specific guidelines for defining and applying duration measurement methodologies, vetting the validity and reliability of measurement tools, and classifying competition level in basketball to address some of the identified knowledge gaps. Creating alignment in best-practice basketball research methodology, terminology and reporting may lead to a more robust understanding of the physical demands associated with the sport, thereby allowing for exploration of other research areas (e.g. injury, performance), and improved understanding and decision making in applying these methods directly with basketball athletes.
... 26 It is known that the risk for injuries and illnesses is higher with increases in load. 36 However, no significant differences for severity scores and time loss were observed between short-term match congestion and regular competition. This is likely because of reduced training load to compensate for multiple matches during the congested schedule. ...
In elite basketball, players are exposed to intensified competition periods when participating in both national and international competitions. How coaches manage training between matches and in reference to match scheduling for a full season is not yet known. Purpose: First, to compare load during short-term match congestion (ie, ≥2-match weeks) with regular competition (ie, 1-match weeks) in elite male professional basketball players. Second, to determine changes in well-being, recovery, neuromuscular performance, and injuries and illnesses between short-term match congestion and regular competition. Methods: Sixteen basketball players (age 24.8 [2.0] y, height 195.8 [7.5] cm, weight 94.8 [14.0] kg, body fat 11.9% [5.0%], VO2max 51.9 [5.3] mL·kg-1·min-1) were monitored during a full season. Session rating of perceived exertion (s-RPE) was obtained, and load was calculated (s-RPE × duration) for each training session or match. Perceived well-being (fatigue, sleep quality, general muscle soreness, stress levels, and mood) and total quality of recovery were assessed each training day. Countermovement jump height was measured, and a list of injuries and illnesses was collected weekly using the adapted Oslo Sports Trauma Research Center Questionnaire on Health Problems. Results: Total load (training sessions and matches; P < .001) and training load (P < .001) were significantly lower for ≥2-match weeks. Significantly higher well-being (P = .01) and less fatigue (P = .001) were found during ≥2-match weeks compared with 1-match weeks. Conclusion: Total load and training load were lower during short-term match congestion compared with regular competition. Furthermore, better well-being and less fatigue were demonstrated within short-term match congestion. This might indicate that coaches tend to overcompensate training load in intensified competition.
... It has been shown that both low and high ACWRs are associated with increased injury risk in several sports, including basketball (Weiss, Allen S, Table III. Injury risk comparisons for the acute workload when considering different load zones (<15th, 15-50th, 50-85th, >85th percentiles McGuigan, & Whatman, 2017), rugby (Hulin, Gabbett, Lawson, Caputi, & Sampson, 2016), and football (Fanchini et al., 2018;Malone et al., 2017;McCall et al., 2018). In addition, in cricket players, excessive loads during a match led to a delayed increased risk of injury up 3-4 weeks after the acute overload (Orchard et al., 2009). ...
Article
This study examined the association and predictive ability of several markers of internal workload on risk of injury in high-performance junior tennis players. Fifteen young, high-level tennis players (9 males, 6 females; age: 17.2 ± 1.1 years; height: 178.5 ± 8.7 cm; body mass: 68.1 ± 4.8 kg) participated in this investigation. Data on injury epidemiology and internal workload during training were obtained for one competitive season. The session-rating of perceived exertion (s-RPE) was used to calculate internal workload markers in absolute (acute workload and chronic workload for 2-weeks, 3-weeks and 4-weeks) and relative terms (acute:chronic workload ratios [ACWR] for 2-weeks, 3-weeks and 4-weeks). Associations and diagnostic power for predicting tennis injuries were examined through generalized estimating equations and receiver operating characteristics analyses. During the season, a total of 40 injuries were recorded, corresponding to 3.5 injuries per 1000 hours of tennis practice. The acute workload was highly associated with injury incidence (P=0.04), as injury risk increased by 1.62 times (95% CI: 1.01 to 2.62) for every increase of 1858.7 arbitrary units (AU) of the workload during the most recent training week. However, acute workload was a poor predictor of injury, and associations between injury and internal workload markers were weak (all P>0.05). These findings demonstrate an association between high values of acute workload and the risk of injury in high-level tennis players. However, a high acute workload is only one of the many factors associated with injury, and by itself, has low predictive ability for injury.
... Basketball has become one of the most popular team sports in the world, with a great economic interest, expressed in the number of games that high-level teams play each season (up to 90 games). These extreme demands in elite team sports require for an extra care in the process of preparing players to perform, ensuring the achievement of optimal overall performances (physical and cognitive) (Clemente, Oliveira et al., 2019;Folgado et al., 2018;Mendes et al., 2018;Rago et al., 2019;Rico-González et al., 2019), avoiding accumulated fatigue and risks of injury (Stojanovic & Ostojic, 2012;Weiss et al., 2017). Thereby, monitoring the players' training and game demands has become a very important task (Vázquez-Guerrero, Fernández-Valdés, Gonçalves et al., 2019;Vázquez-Guerrero, Reche et al., 2018). ...
Article
The aim of this study was to identify the correspondence between the workload demands in training sessions and the game performance from elite basketball players, according to their specific positions. Data were collected from a professional men’s basketball team competing in the Spanish Professional League and Euroleague. Players’ activity during the training sessions was measured using WIMU PRO® and the game statistics were used as a measure of game performance. Cluster analysis allowed to classify the training workload and the game performance, whereas correspondence analysis allowed to explore their relationship. In essence, there was no correspondence from the higher workloads with the best performances, on the contrary, the small forwards’ best performances corresponded to lower training workouts. Despite their importance, the external measures of load need to be complemented with additional (and valid) measures that can be translated to game performance.
... When this does not happen, the most common effect is injury, which is pointed out as a direct consequence of training [19] and expressed by the number of training sessions/games lost [20]. Some studies examined different sports [21][22][23][24] to quantify the relationship between training and injury and showed that high workloads are associated with an increased risk of injury (i.e., overuse injuries). Clemente et al. (2019) described the training methodology applied in team sports as highly variable, both externally and internally [12]. ...
Article
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The purpose of this study was to compare the variations of weekly workload indices of internal and external load measures across the three weeks prior to injury occurrences in trail runners. Twenty-five trail runners (age: 36.23 ± 8.30 years old; body mass: 67.24 ± 5.97 kg; height: 172.12 ± 5.12 cm) were monitored daily for 52 weeks using global positioning systems (GPSs) to determine the total distance covered. Additionally, a rate of perceived exertion (RPE) scale was applied to determine session-RPE (sRPE: RPE multiplied by training time). The accumulated load (AL), acute: chronic workload ratio (ACWR), training monotony (TM), and training strain (TS) indices were calculated weekly for each runner. During the period of analysis, the injury occurrences were recorded. The differences were observed in AL and ACWR for sRPE and training time were significantly greater during the injury week when compared to the previous weeks. Similar evidence was found in TM and TS indices for sRPE, training time, and total distance. Furthermore, no meaningful differences were observed in AL and ACWR for total distance in the weeks prior to injury occurrence. Nevertheless, significant between-subjects variability was found, and this should be carefully considered. For that reason, an individualized analysis of the workload dynamics is recommended, avoiding greater spikes in load by aiming to keep a progressive increment of load without consequences for injury risk.
... Sometimes teams have to play at congested periods of four or five games in ten days [1] and therefore, an adequate load distribution strategy is essential [2]. It is important to follow an adequate load distribution strategy together with an appropriate recovery process to have the maximum players available for each game and to reduce injury risk [3]. ...
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The weekly training management and competition loads are important aspects to optimize the performance of professional basketball players. The objectives of the study were (a) to describe the weekly external load (EL), as well as the internal response (IR), of elite basketball players over two consecutive seasons with a different head coach and (b) to compare weekly loads of different competitive densities. The data were collected from 27 elite players from the same team competing in the Spanish first division league (ACB) and EuroLeague during 2017-2018 and 2018-2019 seasons. EL was measured using microsensor technology to determine PlayerLoad values, expressed in arbitrary units (AU). Session rating of perceived exertion (sRPE) was used for IR quantification. Comparisons between the two seasons and of weeks with different competitive densities were made. The inter-week load variability was moderate-high for both seasons. The highest EL values were measured during the weeks with three games (W3) (W3 > W0 > W2 > W1), while the most demanding week for players' IR was observed during weeks with no competition (W0). Additionally, higher EL (d = 0.31) and IR (d = 0.37) values were observed in season 2018-2019 compared to 2017-2018. The results obtained in this study contributed new data on the internal and external load required by professional basketball players in weeks with different number of games and showed that different coaching strategies may demand a different external and internal workload in consecutive seasons. Furthermore, the results highlighted the need to carry out an adequate load management program.
... 41 Any deficits in training loads that would subsequently fail to prepare the athlete for the demands of competition may further increase the risk of re-injury. 42 It is plausible that the PT cohort of this study may have experienced decrements in individual athletic preparation, and the anecdotally commonly utilized injury management approach of cessation of training may not be effective for this cohort. ...
Article
Objective To identify studies that report three-dimensional (3D) biomechanical analysis of jump-landing tasks in relation to athletes with current patellar tendinopathy (PT), and/or asymptomatic with history of PT or patellar tendon abnormality (PTA) on diagnostic imaging. Methods Five electronic databases were searched. Included articles were required to: (1) investigate the 3D biomechanics of a jump-landing task; (2) be cross-sectional or longitudinal in design; and (3) include participants that had symptomatic PT, were asymptomatic with a history of PT, asymptomatic with PTA on diagnostic imaging and/or asymptomatic with an unknown pathology or PT history. Results Thirty-seven statistically significant jump-landing variables were associated with PT, history of PT and/or PTA. The only consistent variable that could be replicated between studies was knee flexion angle at initial foot-ground contact (IC) and an altered hip flexion/extension strategy during a horizontal land phase of a vertical stop-jump. Conclusion Isolated vertical landings or take-offs alone may not be sensitive enough to identify key jump-landing variables associated with PT, thus clinicians and researchers should incorporate a whole jump-landing task with a horizontal landing component. Sagital plane hip and knee kinematics in a horizontal landing phase appear to provide the most valuable information for evaluating those with PT.
... 19 Monitoring training load with this system should be performed individually and take into consideration recovery strategies when the sportsperson is very tired. 20 Weekly load increases should be individually reviewed, correctly monitoring so as to avoid increases higher than the sportsperson's ability to tolerate them. 21 The aim of this study is to better understand the possible relationships between the application of training loads and the risk of injury in professional women's basketball. ...
Article
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In order to better understand the possible relationships between the application of training loads and the risk of injury in professional women's basketball, four parameters from a professional women's basketball team (N = 11) were analysed: exposure time, number of injuries, rate of perceived exertion (RPE), and workload (sRPE). A total of 3182 h of exposure were registered, 2774 were training hours, and 408 were game hours with a total of 9 time loss injuries. The data obtained from each player was related to the exposure time, injury risk, perception of effort, and workload. Several differences were observed between the injury risk values and the morning RPE (F = 5.0811; p = .032), the sRPE of the morning practices (F = 7.3585; p = .010) and the total time of exposure (F = 3.5055; p = .064). There is also a significant negative relationship between total training time and the number of time-loss (TL) injuries (rho = −.797; p = .003), as well as a possible association between exposure time and a lower risk of TL injury (R² = .645). These findings suggest that an increase in specific exposure time could be associated with a decrease in the risk of time-loss injuries.
... There are 200 million of offi cially registered basketball players in the world, whereas the International Basketball Federation includes 173 countries (Xue, 2002). The salary of basketball players from various leagues ranges from 1 to 25 million dollars per season (Weiss et al., 2017). That is why it is no surprise that a lot of young people around the world associate their further life with sport after having started to play basketball in school or university. ...
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We can define three main factors that become an obstacle on the way of the development of new basketball professionals: insufficient development of certain educational factors; tobacco consumption and the decrease of health quality and activity that is associated with it; engagement with Internet activities. The absence of at least one of these factors would help to effectively influence the young people’s interest in basketball and further development in this field. The participants of the research experiment were 46 teachers of physical education from three Chinese institutes and 765 students (both local and international) from the same institutes. Three surveys were conducted within the course of the research – diagnostic control and additional. Between the diagnostic and control stages, the coaches-participants visited a set of lectures on the formation of the students’ motivation and on the smoking dangers for sportsmen. As a result of the anti-tobacco lectures that were conducted at the end of classes by the coaches-participants of the experiment, the percentage of those students who were ready to give up smoking grew from 9% to 23%.
... This finding contrasts with the originallydescribed ACWR concept, which described an increased injury risk when ACWR was low [3]. We note that few (n=2) [43,65] other studies that have supported this aspect of the concept and question its biological rationale. ...
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Background The acute chronic workload ratio (ACWR) is widely used to evaluate the relationship between training load and health problems. However, both ACWR and health problems can be defined in many ways, and how these methodological choices affect the relationship is unclear.Aim To investigate whether different results emerge when different definitions of ACWR and health problems are used.Methods An online questionnaire was used to collect daily health and training information from 86 elite youth footballers for 105 days. The relationship between players’ training load and health was analysed using a range of different definitions of ACWR and health problem. We used a 21-day and 28-day chronic period, coupled and uncoupled calculations, and exponentially weighted moving average (EWMA) and rolling average (RA). ACWR data were categorized into three bins (low, medium and high) using pre-defined categories and z-scores, and we compared medium to high, medium to low and low to high categories. The outcome was defined in three ways: “all health problems”, “all injuries”, and “new non-contact injuries”. We performed a total of 108 separate random-effects logistic regression analyses.Results We recorded 6250 training days and 196 health problems. Of the 108 analyses performed, 21 (19%) identified a statistically significant (p<0.05) association between ACWR and health problems. A greater proportion of associations were identified when using an “all health problems” definition (33% of analyses), when comparing low and high categories (31%), and using EWMA (28%).Conclusions The relationship between ACWR and health problems was highly dependent on methodological approach.
... However, this format may elicit greater HIA and physiological load; thus, coaches should consider (Ahead of Print) that these added demands might lead to unplanned overload, which may increase injury risk or lead to maladaptive responses. [30][31][32] On the contrary, to increase training loads, the use of GBD with a reduced number of players is strongly suggested, but 3v3 should be avoided during stressful periods as they impose a greater perceptual load. Furthermore, it should be considered that the ND format not only increases the number of passes but also might affect the drill physical demands. ...
Article
Purpose: To examine the physiological, physical, and technical demands of game-based drills (GBDs) with regular dribble (RD) or no dribble (ND) involving a different number of players (3 vs 3, 4 vs 4, and 5 vs 5). Methods: Ten regional-level male basketball players performed 6 full-court GBD formats (each consisting of 3 bouts of 4 min and 2 min rest) on multiple occasions. The physiological and perceptual responses were measured through heart rate and rating of perceived exertion. Video-based time-motion analysis was performed to assess the GBD physical demands. The frequencies of occurrence and the duration were calculated for high-intensity, moderate-intensity, low-intensity, and recovery activities. Technical demands were assessed with a notional-analysis technique. A 2-way repeated-measures analysis of variance was used to assess statistical differences between GBD formats. Results: A greater perceptual response (rating of perceived exertion) was recorded during 3 versus 3 than 5 versus 5 formats (P = .005). Significant interactions were observed for the number of recovery (P = .021), low-intensity activity (P = .007), and all movements (P = .001) completed. Greater time was spent performing low-intensity and high-intensity activities during RD than ND format. Greater technical demands were observed for several variables during 3 versus 3 than 4 versus 4 or 5 versus 5. A greater number of turnovers (P = .027), total (P ≤ .001), and correct passes (P ≤ .001) were recorded during ND than RD format. Conclusions: The number of players predominantly affected the perceptual response to GBD, while both the number of players and rule modification (RD vs ND) affected activities performed during GBD. Reducing the number of players increases the GBD technical elements, while ND format promotes a greater number of turnovers and passes.
... 41 Any deficits in training loads that would subsequently fail to prepare the athlete for the demands of competition may further increase the risk of re-injury. 42 It is plausible that the PT cohort of this study may have experienced decrements in individual athletic preparation, and the anecdotally commonly utilized injury management approach of cessation of training may not be effective for this cohort. ...
Article
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Patellar tendinopathy is a leading cause of morbidity in jump‐landing athletes. Landing mechanics are identified as a factor associated with PT and/or Patellar Tendon Abnormality. This study aimed to identify key jump‐landing variables associated with PT. Thirty‐six junior elite basketball players (men n=18, women n=18) were recruited from a Basketball Australia development camp. Three‐dimensional (3D) kinematic and ground reaction force (GRF) data during a stop‐jump task were collected as well as ultrasound scans of the patellar tendons and recall history of training load data. Mixed‐model factorial analyses of variance were used to determine any significant between‐group differences. Of the 23 participants included for statistical analyses, 11 had normal bilateral patellar tendons (controls) and eight reported PT (currently symptomatic), however the four participants categorised as asymptomatic with PTA were excluded from statistical analyses due to their small sample size. Athletes with PT displayed a similar knee flexion angle at initial foot‐ground contact (IC) and hip extension strategy during a stop‐jump horizontal landing. Despite a similar kinematic technique, athletes with PT utilised a strategy of a longer stance duration phase from IC to peak force. This strategy did not lead to those athletes with PT decreasing their peak vertical GRF nor patellar tendon force during landing but enabled these athletes to land with a lower rate of loading (control 29.4±33.7 vs PT 59.2±39.3 BW.s‐1). Athletes with PT still reported significantly reduced training volume (control 4.85±1.77 vs PT 1.77±1.07 sessions/week; total training time/week control 2.4±1.0 vs PT 1.4±1.1 hours/week).
... Many injuries in the important period for the team may affect the teamʼs poor results. Assessment of the prevalence of overuse injuries and relationship with training load is important for the prevention and treatment 14) . Optimal training load might help reduce injury risk 15) . ...
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Objective: The aim of this study was to investigate basketball related injury and describe medical support for basketball players. Materials: Data were collected retrospectively for ten consecutive seasons from a men’s professional basketball team that presently belongs to the B.League. Cumulative total number of players were 139. Methods: An injury was defined as follows: (1) developed as a result of participation in basketball practice or competition, (2) diagnosed by a team physician or trainer, and (3) resulted in restriction of participation for more than one day after the injury. Results: A total of 190 injuries developed, with an injury rate of 6.4 per 1,000 AE (12.5 and 4.8 for competition and practice, respectively). The risk of injury was significantly higher in competition than that in practice. The ankle was the most common injury site, and ligament sprain was the most common diagnosis. Conclusions: We investigated basketball related injury and described medical support for basketball players. It is important for the team physician to recognize characteristics of basketball injury and specific medical problems.
... As the results of this study show, external and internal variables are complementary methods for monitoring training load. These methods are probably more effective than using only sRPE training load and training volume when the physical fitness level of players is to be assessed 29 . In order to perform at the optimal level in competitions, players need to accumulate a high amount of load, but with a particular distribution. ...
Article
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Injuries in professional soccer are a significant concern for teams, and they are caused amongst others by high training load. This cohort study describes the relationship between workload parameters and the occurrence of non-contact injuries, during weeks with high and low workload in professional soccer players throughout the season. Twenty-one professional soccer players aged 28.3 ± 3.9 yrs. who competed in the Iranian Persian Gulf Pro League participated in this 48-week study. The external load was monitored using global positioning system (GPS, GPSPORTS Systems Pty Ltd) and the type of injury was documented daily by the team's medical staff. Odds ratio (OR) and relative risk (RR) were calculated for non-contact injuries for high- and low-load weeks according to acute (AW), chronic (CW), acute to chronic workload ratio (ACWR), and AW variation (Δ-Acute) values. By using Poisson distribution, the interval between previous and new injuries were estimated. Overall, 12 non-contact injuries occurred during high load and 9 during low load weeks. Based on the variables ACWR and Δ-AW, there was a significantly increased risk of sustaining non-contact injuries (p < 0.05) during high-load weeks for ACWR (OR: 4.67), and Δ-AW (OR: 4.07). Finally, the expected time between injuries was significantly shorter in high load weeks for ACWR [1.25 vs. 3.33, rate ratio time (RRT)] and Δ-AW (1.33 vs. 3.45, RRT) respectively, compared to low load weeks. The risk of sustaining injuries was significantly larger during high workload weeks for ACWR, and Δ-AW compared with low workload weeks. The observed high OR in high load weeks indicate that there is a significant relationship between workload and occurrence of non-contact injuries. The predicted time to new injuries is shorter in high load weeks compared to low load weeks. Therefore, the frequency of injuries is higher during high load weeks for ACWR and Δ-AW. ACWR and Δ-AW appear to be good indicators for estimating the injury risk, and the time interval between injuries.
Article
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Injuries in professional soccer are a significant concern for teams, and they are caused amongst others by high training load. This cohort study describes the relationship between workload parameters and the occurrence of non-contact injuries, during weeks with high and low workload in professional soccer players throughout the season. Twenty-one professional soccer players aged 28.3 ± 3.9 yrs. who competed in the Iranian Persian Gulf Pro League participated in this 48-week study. The external load was monitored using global positioning system (GPS, GPSPORTS Systems Pty Ltd) and the type of injury was documented daily by the team’s medical sta􀀀. Odds ratio (OR) and relative risk (RR) were calculated for non-contact injuries for high- and low-load weeks according to acute (AW), chronic (CW), acute to chronic workload ratio (ACWR), and AW variation (1-Acute) values. By using Poisson distribution, the interval between previous and new injuries were estimated. Overall, 12 non-contact injuries occurred during high load and 9 during low load weeks. Based on the variables ACWR and 1-AW, there was a significantly increased risk of sustaining non-contact injuries (p < 0.05) during high-load weeks for ACWR (OR: 4.67), and 1-AW (OR: 4.07). Finally, the expected time between injuries was significantly shorter in high load weeks for ACWR [1.25 vs. 3.33, rate ratio time (RRT)] and 1-AW (1.33 vs. 3.45, RRT) respectively, compared to low load weeks. The risk of sustaining injuries was significantly larger during high workload weeks for ACWR, and 1-AW compared with low workload weeks. The observed high OR in high load weeks indicate that there is a significant relationship between workload and occurrence of non-contact injuries. The predicted time to new injuries is shorter in high load weeks compared to low load weeks. Therefore, the frequency of injuries is higher during high load weeks for ACWR and 1-AW. ACWR and 1-AW appear to be good indicators for estimating the injury risk, and the time interval between injuries.
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Background Health problems are prevalent in football, and in both elite youth and professional football, players are expected to sustain several health problems per season. Also, at any given time of the season, the prevalence of health problems (both injuries and illnesses) exceeds 40% among elite youth players. Training load has recently emerged as a potential risk factor for health problems in football; subsequently, many teams, particularly those at an elite level, attempt to manage players’ training loads as a preventative measure to mitigate health problem risk. However, the evidence supporting this practice is limited, and its effectiveness is never tested. Therefore, this dissertation aims to improve our understanding of the relationship between training load and health problems and to guide preventative efforts. Methods All studies were performed on Norwegian football players and coahces, Papers I, II and III in elite youth (U19 age category) and Paper IV in professional football. In the first study (Paper I), we registered daily training load and health data to assess methodological issues in the relationship between the Acute:chronic workload ratio (ACWR) and health problems. The second study (Paper II) was a cluster-randomised trial that assessed the effectiveness of a load management programme on health problem prevention. We followed 482 players for a full season, registering their monthly health problem prevalence. In Paper III, we surveyed 250 of the players included in Paper II about their attitudes, beliefs and experiences of load management and health problems. In Paper IV, we assessed the injury characteristics of two different football seasons in the Norwegian premier league. This explorative descriptive study collected injury data from eight teams participating in the 2019 and 2020 seasons. Main results In Paper I, we found 24 (22%) significant associations between ACWR and health problems among the 108 analyses performed. These were spread across various methodological combinations. We did not observe any patterns of combinations that substantially increased the chance of a significant association. There was a considerable variation in the size and precision of the estimated strength of the association. In Paper II, the average prevalence of health problems was 65.7% (61.1% to 70.2%) in the intervention group and 63.8% (60.0% to 67.7%) in the control group. The prevalence was 1.8%-points (-4.1 to 7.7%-points; P=0.55) higher in the intervention group, and there was no reduction in the likelihood of reporting a health problem in the Intervention group (Relative Risk, RR 1.01 (95% CI 0.91 to 1.12 ); P=0.84). In Paper III, we found that most players (88%) think scientific evidence for improved performance is a key facilitator to Implementation. Similarly, the coaches reported that the most crucial facilitator was scientific evidence that the preventive measures were effective (100%). Players reported that the coach’s attitude to preventive measures was important (86%), and similarly, 88% of coaches reported that the player’s attitude was important. In Paper IV, the match incidence was 7.23 per 1000h lower in 2020 (22.82 per 1000h; CI18.07 to 28.44; Incidence Rate Ratio; IRR 0.76) than in 2019 (30.05 per 1000h; CI 24.55 to 36.41); however, this was not a significant difference. There were no differences in either availability, severity or injury burden across the two seasons. Conclusion Based on our findings, we conclude that the relationship between ACWR and health problems is highly affected by the methodological approach, which can lead to p-hacking and cherry-picking of results. Future training load studies should pre-register their definitions, hypotheses, models and report all performed analyses' results. Furthermore, managing training loads using ACWR in a onesize-fits-all approach does not appear to prevent health problems in elite youth football. When implementing future health problem preventive measures, practitioners and researchers should focus on time-efficient interventions and create buy-in from club and federation stakeholders as well as coaches and players by focusing on both performance and prevention. Finally, we found no differences in injuries comparing a match-congested season with a regular one, suggesting a congested season can be a safe alternative.
Article
Limited research exists on the relationship between changes in physical activity levels and injury in children. This study investigated the prognostic relationship between changes in activity measured with the acute:chronic workload ratio (ACWR) and injury in children. We used data from the Childhood Health, Activity, and Motor Performance School Study Denmark (2008-2014), a prospective cohort study of 1,670 children aged 6 to 15. We modelled the relationship between the uncoupled 5-week ACWR and injury defined as patient-reported musculoskeletal pain using generalized additive mixed models. These methods accounted for repeated measures, and improved model fit and precision compared to previous studies that used logistic models. The prognostic model predicted an injury risk of ~3% between decreases in activity level by 60% and increases by 30%. Predicted risk was lower when activity decreased by more than 60% (minimum of 0.5% with no recreational activity). Predicted risk was higher when activity increased by more than 30% (4.5% with 3-fold increase in activity). Girls were at significantly higher risk of injury than boys. We obtained similar patterns but lower absolute risks when we restricted outcome to clinician-diagnosed injury. Predicted increases in injury risk with increasing activity were much lower than previous studies in adults.
Article
In this prospective cohort study, the aim was to examine any association between pre-season training load and Overuse problems (OP) in low back, knee and shoulder in Icelandic elite male handball players. 139 players participated, answering the OSTRC overuse questionnaire weekly during a 6-week period. The training volume and intensity was registered by the coaches. The average weekly OP prevalence for shoulder was 40% (95% CI 36% to 44%), for knee 33% (95% CI 28% to 38%) and for low back 31% (95% CI 26% to 36%). Substantial overuse problems (SOP) were 14% (95% CI 11% to 17%) for shoulder, 11% (95% CI 10% to 12%) for knee and 6% (95%CI 4% to 8%) for low back. The knee was most susceptible for OP with weekly number of training and training hours associated with OP and SOP. For individual training factors, running (OP; OR=1.30, SOP; OR=1.59) and shooting practice (OP; OR=1.82, SOP; OR=3.22) had the highest associations for knee problems. Jumping was associated with OP in low back (OR=4.55). Handball players are most susceptible for OP in knees during their pre-season. Every week, 30% participated with (SOP), affecting their performance and participation.
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The aim of this study was to provide a retrospective overview of injuries during one basketball season and to analyse injury rates and potential risks among professional male and female basketball players in the First Croatian National league. A hundred and forty-two (89 males and 53 females) of 223 basketball players (64%) sustained an injury during the previous season. Body height, total game exposure and total on-court time per game (p<.05) were the identified risk factors for females, while the number of training hours per week was found to be a risk factor for males (p<.05). The highest proportion of injured players was found among centers for men (74%) and forwards (70%) for women. Forty-three percent of injuries were moderately serious, 31% were minor, and 20% serious injuries. Majority (80%) of all injuries were to the lower extremities and men sustained fewer lower extremity injuries compared to women (IRR=0.88 95% CI=0.6 to 1.3). Ankles were the most prevalent injury site for both men and women followed by the knee. Men sustained significantly fewer knee injuries compared to female athletes (IRR=0.44 95% CI=0.17 to 1.11). The most common injury type was ligament injuries (31%), followed by muscle tears/strains (20%). Game incidence injury rate for males was significantly lower than for females (IRR=0.55, p=0.01 [95% CI=0.34-0.89]). Those athletes who, on average, played more than 20 minutes in games were almost twice more exposed to an injury (OR=2.09, 95%CI=1.17,3.72). This is the first descriptive epidemiological study estimating rates and risks of injuries among the Croatian professional basketball players.
Chapter
Though basketball was initially designed as a noncontact sport, its increasingly physical nature has allowed more opportunity for injuries. The biomechanics of basketball-specific movements result in both lower-extremity and upper-extremity injuries. Although sprains are the most common type of injury, basketball players can also suffer strains, contusions, tendinopathies, fractures, head trauma, and cardiac issues. Despite the variability in epidemiological studies of basketball injuries, multiple trends remain consistent. For example, ankle sprains are the most commonly occurring injuries overall and females have higher rates of ACL ruptures and concussions compared to their male counterparts. Injury prevention strategies include neuromuscular training, external supports, player tracking, schedule and rule changes, and preparticipation screening. While lower-extremity injuries are more common among able-bodied athletes, adaptive basketball athletes suffer a higher rate of upper-extremity injuries.
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Purpose: To quantify and compare the internal workloads experienced during the playoffs and regular season in basketball. Methods: A total of 10 professional male basketball players competing in the Italian first division were monitored during the final 6 weeks of the regular season and the entire 6-week playoff phase. Internal workload was quantified using the session rating of perceived exertion (s-RPE) method for all training sessions and games. A 2-way repeated-measures analysis of variance (day type × period) was utilized to assess differences in daily s-RPE between game days, days within 24 hours of games, and days >24 hours from games during the playoffs and regular season. Comparisons in weekly training, game, and total workloads were made between the playoffs and regular season using paired t tests and effect sizes. Results: A significant interaction between day and competitive period for s-RPE was found (P = .003, moderate). Lower s-RPE was apparent during playoff and regular-season days within 24 hours of games than all other days (P < .001, very large). Furthermore, s-RPE across days >24 hours from playoff games was different than all other days (P ≤ .01, moderate-very large). Weekly training (P = .009, very large) and total (P < .001, moderate) s-RPE were greater during the regular season than playoffs, whereas weekly game s-RPE was greater during the playoffs than the regular season (P < .001, very large). Conclusions: This study presents an exploratory investigation of internal workload during the playoffs in professional basketball. Players experienced greater training and total weekly workloads during the regular season than during the playoffs with similar daily game workloads between periods.
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Background The acute:chronic workload ratio (ACWR) is commonly used to manage training load in sports, particularly to reduce injury risk. However, despite its extensive application as a prevention intervention, the effectiveness of load management using ACWR has never been evaluated in an experimental study. Aim To evaluate the effectiveness of a load management intervention designed to reduce the prevalence of health problems among elite youth football players of both sexes. Methods We cluster-randomised 34 elite youth football teams (16 females, 18 males) to an intervention group (18 teams) and a control group (16 teams). Intervention group coaches planned all training based on published ACWR load management principles using a commercially available athlete management system for a complete 10-month season. Control group coaches continued to plan training as normal. The prevalence of health problems was measured monthly in both groups using the Oslo Sports Trauma Research Centre Questionnaire on Health Problems. Results The between-group difference in health problem prevalence (primary outcome) was 1.8%-points (−4.1 to 7.7 %-points; p=0.55) with no reduction in the likelihood of reporting a health problem in the intervention group (relative risk 1.01 (95% CI 0.91 to 1.12); p=0.84) compared with the control group. Conclusions We observed no between-group difference, suggesting that this specific load management intervention was not successful in preventing health problems in elite youth footballers. Trial registration number ISRCTN18177140.
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Background: Limited research exists on the association between increased physical activity and injury in children. Objective: To assess how well different variations of the acute:chronic workload ratio (ACWR) predict injury in children. Methods: We conducted a prospective cohort study using data from 1670 Danish schoolchildren measured over 5.5 years. Coupled 4-week, uncoupled 4-week, and uncoupled 5-week ACWRs were calculated using activity frequency in the past week as the acute load and average weekly activity frequency in the past 4 or 5 weeks as the chronic load. We used new onset pain as a proxy for injury, and modelled its relationship with different ACWR variations using generalized linear and generalized additive models, with and without accounting for repeated measures. Results: The relationship between the ACWR and injury risk was best represented using a generalized additive mixed model for the uncoupled 5-week ACWR. This model predicted an injury risk of ~3% when activity increased by up to 50% or decreased by up to 20% (0.8 <= ACWR <= 1.5). Larger decreases in activity were associated with a decreased injury risk to a minimum of 1.5%. Larger increases in activity were associated with an increased injury risk, from 3% up to a maximum of 6% at ACWR = 5. Girls were at significantly higher risk of injury than boys. Conclusion: Increases in physical activity in children are associated with much lower increases in injury risk compared to previous results in adults.
Article
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Over the past 20 years, research on the training-load–injury relationship has grown exponentially. With the benefit of more data, our understanding of the training-performance puzzle has improved. What were we thinking 20 years ago, and how has our thinking changed over time? Although early investigators attributed overuse injuries to excessive training loads, it has become clear that rapid spikes in training load, above what an athlete is accustomed, explain (at least in part) a large proportion of injuries. In this respect, it appears that overuse injuries may arise from athletes being underprepared for the load they are about to perform. However, a question of interest to both athletic trainers (ATs) and researchers is why some athletes sustain injury at low training loads, while others can tolerate much greater training loads? A higher chronic training load and well-developed aerobic fitness and lower body strength appear to moderate the training-injury relationship and provide a protective effect against spikes in load. The training-performance puzzle is complex and dynamic—at any given time, multiple inputs to injury and performance exist. The challenge facing researchers is obtaining large enough longitudinal data sets to capture the time-varying nature of physiological and musculoskeletal capacities and training-load data to adequately inform injury-prevention efforts. The training-performance puzzle can be solved, but it will take collaboration between researchers and clinicians as well as an understanding that efficacy (ie, how training load affects performance and injury in an idealized or controlled setting) does not equate to effectiveness (ie, how training load affects performance and injury in the real-world setting, where many variables cannot be controlled).
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Purpose: Criticisms of the acute to chronic workload ratio (ACWR) have been that the mathematical coupling inherent in the traditional calculation of the ACWR results in a spurious correlation. The purposes of this commentary are (1) to examine how mathematical coupling causes spurious correlations and (2) to use a case study from actual monitoring data to determine how mathematical coupling affects the ACWR. Methods: Training and competition workload (TL) data were obtained from international-level open-skill (basketball) and closed-skill (weightlifting) athletes before their respective qualifying tournaments for the 2016 Olympic Games. Correlations between acute TL, chronic TL, and the ACWR as coupled/uncoupled variations were examined. These variables were also compared using both rolling averages and exponentially weighted moving averages to account for any potential benefits of one calculation method over another. Results: Although there were some significant differences between coupled and uncoupled chronic TL and ACWR data, the effect sizes of these differences were almost all trivial (g = 0.04-0.21). Correlations ranged from r = .55 to .76, .17 to .53, and .88 to .99 for acute to chronic TL, acute to uncoupled chronic TL, and ACWR to uncoupled ACWR, respectively. Conclusions: There may be low risk of mathematical coupling causing spurious correlations in the TL-injury-risk relationship. Varying levels of correlation seem to exist naturally between acute and chronic TL variables regardless of coupling. The trivial to small effect sizes and large to nearly perfect correlations between coupled and uncoupled AWCRs also imply that mathematical coupling may have little effect on either calculation method, if practitioners choose to apply the ACWR for TL monitoring purposes.
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Background Since 2000, there has been a rapid growth in training load and injury research. In the past 5 years alone, a total of 38 studies (from as many as 24 different research groups, and 11 different sports) have investigated the relationship between loading profiles and injury. Despite the growing body of literature examining training load and injury, there is often a disconnect between this evidence and the actual training programmes prescribed in practice. In this paper, some common myths and misconceptions about training load and its role in injury and performance are reviewed. Myths and misconceptions Common myths relating to training load (the role of training load in injuries, the ‘10% rule’, the influence of spikes and troughs on injury risk and the acute:chronic workload ratio (ACWR)) are explored and discussed. Although the likelihood of injury is increased at an ACWR of ≥1.5 ( on average ), the difference between robust and fragile athletes can largely be explained by three key categories of moderators of the workload—injury relationship; ‘ideal’ age (ie, not too young or too old), physical qualities (eg, well-developed aerobic fitness, speed, repeated-sprint ability and lower body strength) and high chronic training load all decrease the risk associated with a given spike in workload. Rather than focusing solely on the ACWR as has been done in some studies, practitioners are advised to stratify players according to these three moderators of the workload—injury relationship (eg, age, training and injury history, physical qualities), and interpret internal and external load variables in combination with well-being and physical readiness data. When prescribing training load, the practitioner also needs to factor in injury risk factors such as poor biomechanics, academic and emotional stress, anxiety, inadequate sleep and stress-related personality traits. Summary Rapid increases in training and competition workloads and low chronic workloads are associated with greater injury risk. These findings suggest that appropriately staged training programmes may reduce injury risk in athletes. There is an urgent need for randomised controlled trials to test this working hypothesis.
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Background: Between-match recovery time, and acute and chronic workloads likely affect subsequent match-injury risk in elite rugby league players. Methods: Workloads of 28 players throughout two seasons were calculated during short (<7 days), and long (≥7 days) between-match recovery times. 'Acute' workloads (1 week) greater than 'chronic' workloads (4-week rolling average acute workload) resulted in acute:chronic workload ratios above 1. Results: No difference was found between the match-injury risk of short and long between-match recovery periods (7.5±2.5% vs 6.8±2.5%). When players had a short recovery between matches, high chronic workloads (18.9-22.0 km) were associated with a smaller risk of match injury than chronic workloads <18.9 km (relative risk (RR) range 0.27-0.32 (CI 0.08 to 0.92); likelihood range 90-95%, likely). Players who had shorter recovery and acute:chronic workload ratios ≥1.6, were 3.4-5.8 times likely to sustain a match injury than players with lower acute:chronic workload ratios (RR range 3.41-5.80 (CI 1.17 to 19.2); likelihood range 96-99%, very likely). Acute:chronic workload ratios between 1.2 and 1.6 during short between-match recovery times demonstrated a greater risk of match injury than ratios between 1.0 and 1.2 (RR=2.88 (CI 0.97 to 8.55); likelihood=92%, likely). Conclusions: Contrary to the philosophy that high workloads and shorter recovery equate to increased injury risk, our data suggest that high and very-high chronic workloads may protect against match injury following shorter between-match recovery periods. Acute:chronic workload ratios ∼1.5 are associated with a greater risk of match injury than lower acute:chonic workload ratios. Importantly, workloads can be manipulated to decrease the match-injury risk associated with shorter recovery time between matches.
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GOLDILOCKS APPROACH TO TRAINING—NOT TOO LITTLE, NOT TOO MUCH Clinicians or strength and conditioning professionals who prescribe training workloads aim for workloads that are high enough to improve fitness (ie, performance), but not so high as to risk injury. At the extremes, no training results in an unprepared athlete, whereas an overuse injury is, by definition, an error in training prescription.1 Banister et al2 first described an athlete’s training state as the difference between positive (ie, ‘fitness’) and negative (ie, ‘fatigue’) influences. To quantify this concept, ‘fitness’ was represented as the workload (an arbitrary ‘training impulse’) of the athlete over a 3–6 weeks period and ‘fatigue’ was represented by the workload performed over a shorter time frame of 1 week. We recommend the terms ‘chronic workload’ for the longer window of training (ie, Banister’s ‘fitness’) and ‘acute workload’ for the immediate window of training (ie, Banister’s ‘fatigue’) (figure 1). High chronic workloads (ie, intense training), combined with reductions in acute workloads before important competition (ie, taper), would be expected to improve sporting performance.2 ACUTE:CHRONIC WORKLOAD RATIO IS THE IMPORTANT METRIC ‘Training-stress balance’ was a performance concept Andrew Coggan introduced to capture the positive and negative effects of training. Although we used ‘training-stress balance’ in previous publications, 3 we prefer and recommend the concept of ‘acute:chronic workload ratio’. This ratio describes the acute training workload (eg, most recent week’s training load) to the chronic workload (4-week rolling average of acute workload). If chronic workload is high (ie, the athlete has developed ‘fitness’) and the acute workload is low (ie, the athlete is experiencing minimal ‘fatigue’), then the athlete is considered well prepared. Conversely, if acute workload exceeds the chronic workload (ie, the athlete has performed inadequate training to develop ‘fitness’ or workloads have been rapidly increased resulting in ‘fatigue’), then the athlete is considered underprepared and likely at an increased risk of injury (see figure 1). As such, the acute:chronic workload ratio indicates both the athlete’s risk of injury and preparedness to perform. ATHLETE WORKLOADS CAN PREDICT INJURY Individual sport athletes had largely been the focus of research and application of the acute:chronic workload ratio. The measures of training load were used for periodisation plans and tapering for performance. 2 Several studies have applied this workload model to predict injuries in team sport athletes. In studies of rugby league4 players, high chronic workloads were associated with a reduced risk of injury, while large ‘spikes’ in acute workloads relative to chronic workloads were associated with increased risk of injury. A consistent theme throughout this paper was the importance of progressively and systematically increasing workloads to minimise the risk of injury. Importantly, the predictive ability of this approach is very high; we reported a positive likelihood as high as 70 times.5 RETURNING SAFELY FROM INJURY REQUIRES CONSIDERATION OF THE WORKLOAD COMPLETED While various functional tests are commonly used to assess the progress of an injured athlete, there is little evidence that they can predict safe return to play. It is often noted that the best predictor of a musculoskeletal injury is previous injury history.6 This could be due, at least in part, to the reduced fitness (chronic workload) caused by the recent injury-induced lay-off. Specifically, the importance of the amount of training performed in the current week (ie, acute workload) relative to training the athlete has been prepared for over the preceding 4 weeks (ie, chronic workload) is crucial.6 Given its association with injury risk, the acute: chronic workload ratio may prove critical when determining return to train, play and ‘compete’ rehabilitation plans for many injuries that have resulted in substantially decreased training load over 2 or more weeks. RAPID ‘SPIKES’ IN TRAINING WORKLOAD IS THE PROBLEM, WHILE CONSISTENT TRAINING IS THE SOLUTION Several studies have demonstrated greater injury rates with higher training workloads. 7 However, it should be noted that the benefits of training (ie, well-developed physical qualities and the application of the training workload itself ) may provide resilience to athletes, resulting in protection from injuries.7 Potentially, a carefully staged programme that culminates in high workloads makes for durable athletes, while attempting to cut corners and ‘spike’ to high workloads results in high injury risk. Indeed, weekly increases in workloads of >10% are associated with increased injury risk, while smaller increases in workloads result in much lower risk of injury.7 IT’S NOT THE DESTINATION, IT’S THE (WORKLOAD) JOURNEY THAT MATTERS To optimally prepare for competition demands, athletes need to (gradually) increase their workloads so that their fitness (chronic workload) is sufficient to overcome acute fatigue demands. We hope that sport science, coaches, strength and conditioning, and health professionals see the value in the acute:chronic workload ratio and incorporate this form of monitoring into their day-to-day training environment. Contributors The initial concepts and drafts were formulated by TJG. BTH, PB and RW contributed equally to several drafts of the editorial. Competing interests None declared. Provenance and peer review Not commissioned; externally peer reviewed. 1School of Exercise Science, Australian Catholic University, Brisbane, Queensland, Australia; 2School of Human Movement Studies, University of Queensland, Brisbane, Queensland, Australia; 3Centre for Human and Applied Physiology, School of Medicine, University of Wollongong, Wollongong, New South Wales, Australia; 4High Performance Unit, Essendon Football Club, Melbourne, Victoria, Australia; 5School of Allied Health Sciences, Griffith University, Gold Coast, Queensland, Australia; 6Aspetar Orthopaedic and Sports Medicine Hospital, Doha, Qatar Correspondence to Dr Tim J Gabbett, School of Exercise Science, Australian Catholic University, 1100 Nudgee Road, Brisbane, QLD 4014, Australia; tim_gabbett@yahoo.com.au 444 Br J Sports Med April 2016 Vol 50 No 8 Editorials Downloaded from http://bjsm.bmj.com/ on April 5, 2016 - Published by group.bmj.com To cite Gabbett TJ, Hulin BT, Blanch P, et al. Br J Sports Med 2016;50:444–445. Accepted 31 October 2015 Published Online First 21 January 2016 Br J Sports Med 2016;50:444–445. doi:10.1136/bjsports-2015-095567 REFERENCES 1 O’Toole ML. Prevention and treatment of injuries to runners. Med Sci Sports Exerc 1992;9(Suppl):S360–3. 2 Banister EW, Calvert TW, Savage MV, et al. A systems model of training for athletic performance. Aust J Sports Med 1975;7:57–61. 3 Hulin BT, Gabbett TJ, Blanch P, et al. Spikes in acute workload are associated with increased injury risk in elite cricket fast bowlers. Br J Sports Med 2014;48:708–12. 4 Hulin BT, Gabbett TJ, Lawson DW, et al. The acute: chronic workload ratio predicts injury: high chronic workload may decrease injury risk in elite rugby league players. Br J Sports Med 2016;50:231–6. 5 Gabbett TJ. The development and application of an injury prediction model for noncontact, soft-tissue injuries in elite collision sport athletes. J Strength Cond Res 2010;24:2593–603. 6 Blanch P, Gabbett TJ. Has the athlete trained enough to return to play safely? The acute:chronic workload ratio permits clinicians to quantify a player's risk of subsequent injury. Br J Sports Med 2016;50:471–5. 7 Gabbett TJ. The training-injury prevention paradox: should athletes be training smarter and harder? Br J Sports Med 2016;50:273–80. Figure 1 Acute and chronic workloads and the calculation of the acute:chronic workload ratio as a method of monitoring training in team sport athletes. (A) Each bar represents an acute workload. In this instance, the acute workload represents 1 week of training. (B) The 4-week rolling average of acute workloads represents a chronic workload. (C) The chronic workload at week 4 represents the rolling average of acute workloads performed over the previous 3 weeks, plus the current week (ie, weeks 1, 2, 3 and 4). Since an additional acute workload is prescribed in week 5, the new chronic workload will represent the rolling average acute workload of weeks 2, 3, 4 and 5. (D) Acute and chronic workload and the acute: chronic workload ratio over an entire playing season.
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Background: There is dogma that higher training load causes higher injury rates. However, there is also evidence that training has a protective effect against injury. For example, team sport athletes who performed more than 18 weeks of training before sustaining their initial injuries were at reduced risk of sustaining a subsequent injury, while high chronic workloads have been shown to decrease the risk of injury. Second, across a wide range of sports, well-developed physical qualities are associated with a reduced risk of injury. Clearly, for athletes to develop the physical capacities required to provide a protective effect against injury, they must be prepared to train hard. Finally, there is also evidence that under-training may increase injury risk. Collectively, these results emphasise that reductions in workloads may not always be the best approach to protect against injury. Main thesis: This paper describes the 'Training-Injury Prevention Paradox' model; a phenomenon whereby athletes accustomed to high training loads have fewer injuries than athletes training at lower workloads. The Model is based on evidence that non-contact injuries are not caused by training per se, but more likely by an inappropriate training programme. Excessive and rapid increases in training loads are likely responsible for a large proportion of non-contact, soft-tissue injuries. If training load is an important determinant of injury, it must be accurately measured up to twice daily and over periods of weeks and months (a season). This paper outlines ways of monitoring training load ('internal' and 'external' loads) and suggests capturing both recent ('acute') training loads and more medium-term ('chronic') training loads to best capture the player's training burden. I describe the critical variable-acute:chronic workload ratio)-as a best practice predictor of training-related injuries. This provides the foundation for interventions to reduce players risk, and thus, time-loss injuries. Summary: The appropriately graded prescription of high training loads should improve players' fitness, which in turn may protect against injury, ultimately leading to (1) greater physical outputs and resilience in competition, and (2) a greater proportion of the squad available for selection each week.
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The return to sport from injury is a difficult multifactorial decision, and risk of reinjury is an important component. Most protocols for ascertaining the return to play status involve assessment of the healing status of the original injury and functional tests which have little proven predictive ability. Little attention has been paid to ascertaining whether an athlete has completed sufficient training to be prepared for competition. Recently, we have completed a series of studies in cricket, rugby league and Australian rules football that have shown that when an athlete's training and playing load for a given week (acute load) spikes above what they have been doing on average over the past 4 weeks (chronic load), they are more likely to be injured. This spike in the acute:chronic workload ratio may be from an unusual week or an ebbing of the athlete's training load over a period of time as in recuperation from injury. Our findings demonstrate a strong predictive (R(2)=0.53) polynomial relationship between acute:chronic workload ratio and injury likelihood. In the elite team setting, it is possible to quantify the loads we are expecting athletes to endure when returning to sport, so assessment of the acute:chronic workload ratio should be included in the return to play decision-making process.
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Aim: Investigate whether acute workload (1 week total distance) and chronic workload (4-week average acute workload) predict injury in elite rugby league players. Methods: Data were collected from 53 elite players over two rugby league seasons. The ‘acute:chronic workload ratio’ was calculated by dividing acute workload by chronic workload. A value of greater than 1 represented an acute workload greater than chronicworkload. All workload data were classified into discrete ranges by z-scores. Results Compared with all other ratios, a very-high acute:chronic workload ratio (≥2.11) demonstrated the greatest risk of injury in the current week (16.7% injury risk) and subsequent week (11.8% injury risk). High chronic workload (>16 095 m) combined with a very high 2-week average acute:chronic workload ratio (≥1.54) was associated with the greatest risk of injury (28.6% injury risk). High chronic workload combined with a moderate workload ratio (1.02–1.18) had a smaller risk of injury than low chronic workload combined with several workload ratios (relative risk range from 0.3 to 0.7×/÷1.4 to 4.4; likelihood range=88–94%, likely). Considering acute and chronic workloads in isolation (ie, not as ratios) did not consistently predict injury risk. Conclusions: Higher workloads can have either positive or negative influences on injury risk in elite rugby league players. Specifically, compared with players who have a low chronic workload, players with a high chronic workload are more resistant to injury with moderate-low through moderate-high (0.85–1.35)acute:chronic workload ratios and less resistant to injury when subjected to ‘spikes’ in acute workload, that is, very-high acute:chronic workload ratios ∼1.5.
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To determine if the comparison of acute and chronic workload is associated with increased injury risk in elite cricket fast bowlers. Data were collected from 28 fast bowlers who completed a total of 43 individual seasons over a 6-year period. Workloads were estimated by summarising the total number of balls bowled per week (external workload), and by multiplying the session rating of perceived exertion by the session duration (internal workload). One-week data (acute workload), together with 4-week rolling average data (chronic workload), were calculated for external and internal workloads. The size of the acute workload in relation to the chronic workload provided either a negative or positive training-stress balance. A negative training-stress balance was associated with an increased risk of injury in the week after exposure, for internal workload (relative risk (RR)=2.2 (CI 1.91 to 2.53), p=0.009), and external workload (RR=2.1 (CI 1.81 to 2.44), p=0.01). Fast bowlers with an internal workload training-stress balance of greater than 200% had a RR of injury of 4.5 (CI 3.43 to 5.90, p=0.009) compared with those with a training-stress balance between 50% and 99%. Fast bowlers with an external workload training-stress balance of more than 200% had a RR of injury of 3.3 (CI 1.50 to 7.25, p=0.033) in comparison to fast bowlers with an external workload training-stress balance between 50% and 99%. These findings demonstrate that large increases in acute workload are associated with increased injury risk in elite cricket fast bowlers.
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Physiological assessment of soccer training usually refers to the measurement of anatomical, physiological, biochemical and functional changes specific to the sport discipline (training outcome). The quality, quantity and organization of physical exercises (training process) are, on the other hand, usually described by the external work imposed by the coach on his or her athletes. In this review, we demonstrate that this approach is not appropriate in soccer, as training is often based on group exercises. The physiological stress (internal load) induced by such training often differs between individuals. Here, we present some physiological laboratory-based tests and field tests used to evaluate training outcomes in soccer, together with methods based on heart rate and perceived exertion to quantify internal load imposed during training. The integrated physiological assessment of both training outcome and process allows researchers: (1) to improve interpretation of physical tests used to verify the effectiveness of training programmes; (2) to evaluate the organization of the training load in order to design periodization strategies; (3) to identify athletes who are poor responders; (4) to control the compliance of the training completed to that planned by the coach; and (5) to modify the training process before the assessment of its outcome, thus optimizing soccer performance.
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