Architectural changes of the biceps femoris long head after concentric or eccentric training

Purpose: To determine i) the architectural adaptations of the biceps femoris long head (BFlf) following concentric or eccentric strength training interventions; ii) the time course of adaptation during training and detraining. Methods: Participants in this randomized controlled trial (control [n=28], concentric training group [n=14], eccentric training group [n=14], males) completed a 4-week control period, followed by 6 weeks of either concentric- or eccentric-only knee flexor training on an isokinetic dynamometer and finished with 28 days of detraining. Architectural characteristics of BFlf were assessed at rest and during graded isometric contractions utilizing two-dimensional ultrasonography at 28 days pre-baseline, baseline, days 14, 21 and 42 of the intervention and then again following 28 days of detraining. Results: BFlf fascicle length was significantly longer in the eccentric training group (p < 0.05, d range: 2.65 to 2.98) and shorter in the concentric training group (p < 0.05, d range: -1.62 to -0.96) after 42 days of training compared to baseline at all isometric contraction intensities. Following the 28-day detraining period, BFlf fascicle length was significantly reduced in the eccentric training group at all contraction intensities compared to the end of the intervention (p < 0.05, d range: -1.73 to -1.55). There was no significant change in fascicle length of the concentric training group following the detraining period. Conclusions: These results provide evidence that short term resistance training can lead to architectural alterations in the BFlf. In addition, the eccentric training-induced lengthening of BFlf fascicle length was reversed and returned to baseline values following 28 days of detraining. The contraction mode specific adaptations in this study may have implications for injury prevention and rehabilitation

Modeling the Risk of Team Sport Injuries: A Narrative Review of Different Statistical Approaches

Injuries are a common occurrence in team sports and can have significant financial, physical and psychological consequences for athletes and their sporting organizations. As such, an abundance of research has attempted to identify factors associated with the risk of injury, which is important when developing injury prevention and risk mitigation strategies. There are a number of methods that can be used to identify injury risk factors. However, difficulty in understanding the nuances between different statistical approaches can lead to incorrect inferences and decisions being made from data. Accordingly, this narrative review aims to (1) outline commonly implemented methods for determining injury risk, (2) highlight the differences between association and prediction as it relates to injury and (3) describe advances in statistical modeling and the current evidence relating to predicting injuries in sport. Based on the points that are discussed throughout this narrative review, both researchers and practitioners alike need to carefully consider the different types of variables that are examined in relation to injury risk and how the analyses pertaining to these different variables are interpreted. There are a number of other important considerations when modeling the risk of injury, such as the method of data transformation, model validation and performance assessment. With these technical considerations in mind, researchers and practitioners should consider shifting their perspective of injury etiology from one of reductionism to one of complexity. Concurrently, research implementing reductionist approaches should be used to inform and implement complex approaches to identifying injury risk. However, the ability to capture large injury numbers is a current limitation of sports injury research and there has been a call to make data available to researchers, so that analyses and results can be replicated and verified. Collaborative efforts such as this will help prevent incorrect inferences being made from spurious data and will assist in developing interventions that are underpinned by sound scientific rationale. Such efforts will be a step in the right direction of improving the ability to identify injury risk, which in turn will help improve risk mitigation and ultimately the prevention of injuries

The dose-response of the nordic hamstring exercise on biceps femoris architecture and eccentric knee flexor strength : A randomized interventional trial

Purpose: To examine the dose–response of the Nordic hamstring exercise (NHE) on biceps femoris long head (BFlh) architecture and eccentric knee flexor strength. Design: Randomized interventional trial. Methods: Forty recreationally active males completed a 6-week NHE training program consisting of either intermittent low volumes (group 1; n = 10), low volumes (group 2; n = 10), initial high volumes followed by low volumes (group 3; n = 10), or progressively increasing volumes (group 4; n = 10). A 4-week detraining period followed each program. Muscle architecture was assessed weekly during training and after 2 and 4 weeks of detraining. Eccentric knee flexor strength was assessed preintervention and postintervention and after 2 and 4 weeks of detraining. Results: Following 6 weeks of training, BFlh fascicle length (FL) increased in group 3 (mean difference = 0.83 cm, d = 0.45, P = .027, +7%) and group 4 (mean difference = 1.48 cm, d = 0.94, P = .004, +14%). FL returned to baseline following detraining in groups 3 and 4. Strength increased in group 2 (mean difference = 53.6 N, d = 0.55, P = .002, +14%), group 3 (mean difference = 63.4 N, d = 0.72, P = .027, +17%), and group 4 (mean difference = 74.7, d = 0.83, P = .006, +19%) following training. Strength returned to baseline following detraining in groups 2 and 3 but not in group 4. Conclusions: Initial high volumes of the NHE followed by lower volumes, as well as progressively increasing volumes, can elicit increases in BFlh FL and eccentric knee flexor strength. Low volumes of the NHE were insufficient to increase FL, although as few as 48 repetitions in 6 weeks did increase strength

Session availability as a result of prior injury impacts the risk of subsequent injury in elite male Australian footballers

Prior injury is a commonly identified risk factor for subsequent injury. However, a binary approach to classifying prior injury (i.e., yes/no) is commonly implemented and may constrain scientific findings, as it is possible that variations in the amount of time lost due to an injury will impact subsequent injury risk to differing degrees. Accordingly, this study investigated whether session availability, a surrogate marker of prior injury, influenced the risk of subsequent non-contact lower limb injury in Australian footballers. Data were collected from 62 male elite Australian footballers throughout the 2015, 2016, and 2017 Australian Football League seasons. Each athlete’s participation status (i.e., full or missed/modified) and any injuries that occurred during training sessions/matches were recorded. As the focus of the current study was prior injury, any training sessions/matches that were missed due to reasons other than an injury (e.g., load management, illness and personal reasons) were removed from the data prior to all analyses. For every Monday during the in-season periods, session availability (%) in the prior 7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, and 84 days was determined as the number of training sessions/matches fully completed (injury free) relative to the number of training sessions/matches possible in each window. Each variable was modeled using logistic regression to determine its impact on subsequent injury risk. Throughout the study period, 173 non-contact lower limb injuries that resulted in at least one missed/modified training session or match during the in-season periods occurred. Greater availability in the prior 7 days increased injury probabilities by up to 4.4%. The impact of session availability on subsequent injury risk diminished with expanding windows (i.e., availability in the prior 14 days through to the prior 84 days). Lesser availability in the prior 84 days increased injury probabilities by up to 14.1%, only when coupled with greater availability in the prior 7 days. Session availability may provide an informative marker of the impact of prior injury on subsequent injury risk and can be used by coaches and clinicians to guide the progression of training, particularly for athletes that are returning from long periods of injury

Proteome-wide analysis and diel proteomic profiling in the cyanobacterium Arthrospira platensis PCC 8005

The filamentous cyanobacteriumArthrospira platensishas a long history of use as a food supply and it has been used by the European Space Agency in the MELiSSA project, an artificial microecosystem which supports life during long-term manned space missions. This study assesses progress in the field of cyanobacterial shotgun proteomics and light/dark diurnal cycles by focusing onArthrospira platensis. Several fractionation workflows including gel-free and gel-based protein/peptide fractionation procedures were used and combined with LC-MS/MS analysis, enabling the overall identification of 1306 proteins, which represents 21% coverage of the theoretical proteome. A total of 30 proteins were found to be significantly differentially regulated under light/dark growth transition. Interestingly, most of the proteins showing differential abundance were related to photosynthesis, the Calvin cycle and translation processes. A novel aspect and major achievement of this work is the successful improvement of the cyanobacterial proteome coverage using a 3D LC-MS/MS approach, based on an immobilized metal affinity chromatography, a suitable tool that enabled us to eliminate the most abundant protein, the allophycocyanin. We also demonstrated that cell growth follows a light/dark cycle inA. platensis. This preliminary proteomic study has highlighted new characteristics of theArthrospira platensisproteome in terms of diurnal regulation

Predictive modelling of self-reported wellness and the risk of injury in elite Australian footballers

Injuries are a common occurrence in team sports and can have significant financial, physical and psychological consequences for athletes and their sporting organisations. As such, an abundance of research has attempted to identify factors associated with the risk of injury, which is important when developing injury prevention and risk mitigation strategies. Traditionally, research has implemented reductionist approaches to identify injury risk factors. These reductionist methodologies assume that all the parts of a system (in this case, injury aetiology) can be broken down and examined individually and then summed together to represent the system as a whole. Reductionist approaches are useful in establishing associations between specific factors and the risk of injury. However, in order to predict the occurrence of injuries at an individual level, complex approaches should be implemented. In light of this, machine learning has been suggested as an appropriate method of applying complex approaches to the prediction of injuries in sport. Machine learning is a field of computer science which involves building algorithms to learn from data and make predictions without being programmed what to look for or where to look for it. Whilst machine learning cannot be used to establish causal relationships between specific factors and the occurrence of injuries, it differs from reductionist methodologies in that it has the ability to identify the complex, non-linear interactions that occur amongst risk factors. Study 1 (Chapter 4) aimed to utilise machine learning methods to predict the occurrence of hamstring strain injuries (HSIs) in elite Australian footballers. Hamstring strain injury is the most common injury in elite Australian football and three of the most consistently identified risk factors for HSI are increasing age, prior HSI and low levels of eccentric knee flexor strength. While some iterations of the predictive models achieved near perfect performance (maximum area under the curve [AUC] = 0.92), others performed worse than random chance (minimum AUC = 0.24). It was concluded that age, previous HSI and eccentric knee flexor strength data could not be used to identify Australian footballers at an increased risk of HSI with any consistency, despite these factors being highly associated with the risk of HSI. It is suggested that more observed injuries, in addition to more frequent measures of the variables included in the models, may have improved the performance of the predictive models in Study 1. To overcome the limitations acknowledged in Study 1, Study 2 (Chapter 5) investigated whether more frequent measures of the impact of prior injury (in the form of session availability), in addition to a greater number of observed injuries (albeit non-specific pathologies), improved the ability to identify injury risk. It was observed that greater session availability in the previous 7 days increased injury probabilities by up to 4.4%. Additionally, lesser session availability in the previous 84 days increased injury probabilities by up to 14.1%, only when coupled with greater availability in the previous 7 days. It was concluded that session availability may provide an informative marker of the impact of prior injury on subsequent injury risk and can be used by practitioners to guide the progression of training, particularly for athletes that are returning from long periods of injury. Study 1 and Study 2 implemented complex approaches in an attempt to improve injury risk identification at an individual level. Despite the findings of Study 1 and Study 2, quantifying injury risk on a daily basis remains a complex and challenging task for practitioners working in Australian football. Commonly implemented tools such as self-reported wellness questionnaires provide a much more accessible measure of athletes’ wellbeing and how they are responding to the demands of training/competition. Whilst improving the ability to estimate injury risk at an individual level is an important focus area, it may also be important to determine the level of information that more accessible and more frequently measured variables (such as self-reported wellness) provide regarding injury risk. To make this determination, however, it is also necessary to understand the factors that directly influence self-reported wellness. Accordingly, Study 3 (Chapter 6) aimed to investigate the factors that impact wellness in elite Australian footballers. Measures of external load examined on their own were able to explain changes in wellness to a large degree (root mean square error = 1.55, 95% confidence intervals = 1.52 to 1.57). However, there was a proportion of wellness that could not be explained by external loads. It is suggested that examining the interaction between external training loads and self-reported wellness may assist practitioners in their load management strategies. However, there is limited research investigating the interaction between external loads and wellness and the impact this information may have on subsequent injury risk. Accordingly, Study 4 (Chapter 7) aimed to investigate the ability of external load data, session availability data and self-reported wellness data, as well as the interaction between the three, to identify the risk of lower limb non-contact injuries in elite Australian footballers. The model with the least input variables (athlete ID and session type) displayed the highest predictive ability (AUC = 0.76, Akaike information criterion [AIC] = 479, Brier score = 0.009). The models built using external load, session availability and wellness data all displayed similar predictive ability (AUCs = 0.72 to 0.75, AICs = 477 to 478, Brier scores = 0.009 to 0.009). Despite observing higher predictive performance compared to previous research, the addition of external load, session availability and wellness data, as well as demographic and pre-season external load data, did not improve the ability to predict lower limb non-contact injuries in Study 4. Overall, this program of research displayed a limited ability to predict injuries in elite Australian football. The findings of this thesis highlight a need for a larger number of observed injuries when implementing predictive modelling strategies to identify injury risk at an individual level. Despite this, the predictive modelling strategies implemented in this thesis may assist researchers and practitioners in better understanding the relationships that exist between variables that are commonly collected, analysed and interpreted. Whilst the efficacy of complex approaches and their application in sports research may warrant further investigation, researchers and practitioners alike need to strongly consider the limitations of input data and the predictive modelling strategies used to analyse these data when conducting (as well as interpreting) future research

Modeling the risk of team sport injuries : A narrative review of different statistical approaches

Injuries are a common occurrence in team sports and can have significant financial, physical and psychological consequences for athletes and their sporting organizations. As such, an abundance of research has attempted to identify factors associated with the risk of injury, which is important when developing injury prevention and risk mitigation strategies. There are a number of methods that can be used to identify injury risk factors. However, difficulty in understanding the nuances between different statistical approaches can lead to incorrect inferences and decisions being made from data. Accordingly, this narrative review aims to (1) outline commonly implemented methods for determining injury risk, (2) highlight the differences between association and prediction as it relates to injury and (3) describe advances in statistical modeling and the current evidence relating to predicting injuries in sport. Based on the points that are discussed throughout this narrative review, both researchers and practitioners alike need to carefully consider the different types of variables that are examined in relation to injury risk and how the analyses pertaining to these different variables are interpreted. There are a number of other important considerations when modeling the risk of injury, such as the method of data transformation, model validation and performance assessment. With these technical considerations in mind, researchers and practitioners should consider shifting their perspective of injury etiology from one of reductionism to one of complexity. Concurrently, research implementing reductionist approaches should be used to inform and implement complex approaches to identifying injury risk. However, the ability to capture large injury numbers is a current limitation of sports injury research and there has been a call to make data available to researchers, so that analyses and results can be replicated and verified. Collaborative efforts such as this will help prevent incorrect inferences being made from spurious data and will assist in developing interventions that are underpinned by sound scientific rationale. Such efforts will be a step in the right direction of improving the ability to identify injury risk, which in turn will help improve risk mitigation and ultimately the prevention of injuries

Running exposure is associated with the risk of hamstring strain injury in elite Australian footballers

Background To investigate the association between running exposure and the risk of hamstring strain injury (HSI) in elite Australian footballers. Methods Elite Australian footballers (n=220) from 5 different teams participated. Global positioning system (GPS) data were provided for every athlete for each training session and match for the entire 2015 season. The occurrences of HSIs throughout the study period were reported. Receiver operator characteristic curve analyses were performed and the relative risk (RR) of subsequent HSI was calculated for absolute and relative running exposure variables related to distance covered above 10 and 24 km/hour in the preceding week/s. Results 30 prospective HSIs occurred. For the absolute running exposure variables, weekly distance covered above 24 km/hour (>653 m, RR=3.4, 95% CI 1.6 to 7.2, sensitivity=0.52, specificity=0.76, area under the curve (AUC)=0.63) had the largest influence on the risk of HSI in the following week. For the relative running exposure variables, distance covered above 24 km/hour as a percentage of distance covered above 10 km/hour (>2.5%, RR=6.3, 95% CI 1.5 to 26.7, sensitivity=0.93, specificity=0.34, AUC=0.63) had the largest influence on the risk of HSI in the following week. Despite significant increases in the RR of HSI, the predictive capacity of these variables was limited. Conclusions An association exists between absolute and relative running exposure variables and elite Australian footballers' risk of subsequent HSI, with the association strongest when examining data within 7–14 days. Despite this, the use of running exposure variables displayed limited clinical utility to predict HSI at the individual level

Factors that Impact Self-reported Wellness Scores in Elite Australian Footballers

Introduction This study aimed to 1) identify the impact of external load variables on changes in wellness and 2) identify the impact of age, training/playing history, strength levels, and preseason loads on changes in wellness in elite Australian footballers. Methods Data were collected from one team (45 athletes) during the 2017 season. Self-reported wellness was collected daily (4, best score possible; 28, worst score possible). External load/session availability variables were calculated using global positioning systems and session availability data from every training session and match. Additional variables included demographic data, preseason external loads, and strength/power measures. Linear mixed models were built and compared using root mean square error (RMSE) to determine the impact of variables on wellness. Results The external load variables explained wellness to a large degree (RMSE = 1.55, 95% confidence intervals = 1.52 to 1.57). Modeling athlete ID as a random effect appeared to have the largest impact on wellness, improving the RMSE by 1.06 points. Aside from athlete ID, the variable that had the largest (albeit negligible) impact on wellness was sprint distance covered across preseason. Every additional 2.1 km covered across preseason worsened athletes’ in-season wellness scores by 1.2 points (95% confidence intervals = 0.0–2.3). Conclusions The isolated impact of the individual variables on wellness was negligible. However, after accounting for the individual athlete variability, the external load variables examined collectively were able to explain wellness to a large extent. These results validate the sensitivity of wellness to monitor individual athletes’ responses to the external loads imposed on them

Screening hamstring injury risk factors multiple times in a season does not improve the identification of future injury risk

Purpose To determine if eccentric knee flexor strength and biceps femoris long head (BFlh) fascicle length were associated with prospective hamstring strain injury (HSI) in professional Australian Football players, and if more frequent assessments of these variables altered the association with injury risk. Methods Across two competitive seasons, 311 Australian Football players (455 player seasons) had their eccentric knee flexor strength during the Nordic hamstring exercise and BFlh architecture assessed at the start and end of preseason and in the middle of the competitive season. Player age and injury history were also collected in preseason. Prospective HSIs were recorded by team medical staff. Results Seventy-four player seasons (16%) sustained an index HSI. Shorter BFlh fascicles (9%), when measured at multiple time points, was (RR, 1.8; 95% CI, 1.1–3.1). Prior HSI had the strongest univariable association with prospective HSI (RR, 2.9; 95% CI, 1.9–4.3). Multivariable logistic regression models identified a combination of prior HSI, BFlh architectural variables and between-limb imbalance in eccentric knee flexor strength as optimal input variables; however, their predictive performance did not improve with increased measurement frequency (area under the curve, 0.681–0.726). Conclusions More frequent measures of eccentric knee flexor strength and BFlh architecture across a season did not improve the ability to identify which players would sustain an HSI