Diagnosed concussion is associated with increased risk for lower extremity injury in community rugby players

Hunzinger, Katherine J.; Costantini, Katelyn; Swanik, C. Buz; Buckley, Thomas A.

doi:10.1016/j.jsams.2020.10.013

Cited by 18 publications

(22 citation statements)

References 24 publications

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“…The relationship between concussion and lower extremity injury has been extensively studied in the sports medicine literature, 10 , 15 , 16 with a number of hypotheses for the underlying mechanism, including resultant deficiencies in dynamic balance, neuromuscular control, cognitive accuracy, and gait performance. 4 , 14 , 27 , 31 , 40 This observation is corroborated by our study, which identified a concussion history as a significant risk factor in both the multivariate logistic regression and the machine learning models.…”

Section: Discussionmentioning

confidence: 99%

“…Bootstrapping has been found to optimize both model bias and variance and improve overall performance compared with internal validation through splitting the data into a partition of training and holdout sets. 15 In addition, a gradient boosted ensemble model of the 5 candidate models was constructed and trained, similarly through 0.623 bootstrapping. The advantages of ensemble modeling include decreasing variance and bias as well as improving predictions, while disadvantages include increased memory requirements and reduced speed of implementation.…”

Section: Detailed Machine-learning Modeling Workflowmentioning

confidence: 99%

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Machine Learning for Predicting Lower Extremity Muscle Strain in National Basketball Association Athletes

Pareek

Lavoie-Gagne

et al. 2022

Orthopaedic Journal of Sports Medicine

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Background: In professional sports, injuries resulting in loss of playing time have serious implications for both the athlete and the organization. Efforts to quantify injury probability utilizing machine learning have been met with renewed interest, and the development of effective models has the potential to supplement the decision-making process of team physicians. Purpose/Hypothesis: The purpose of this study was to (1) characterize the epidemiology of time-loss lower extremity muscle strains (LEMSs) in the National Basketball Association (NBA) from 1999 to 2019 and (2) determine the validity of a machine-learning model in predicting injury risk. It was hypothesized that time-loss LEMSs would be infrequent in this cohort and that a machine-learning model would outperform conventional methods in the prediction of injury risk. Study Design: Case-control study; Level of evidence, 3. Methods: Performance data and rates of the 4 major muscle strain injury types (hamstring, quadriceps, calf, and groin) were compiled from the 1999 to 2019 NBA seasons. Injuries included all publicly reported injuries that resulted in lost playing time. Models to predict the occurrence of a LEMS were generated using random forest, extreme gradient boosting (XGBoost), neural network, support vector machines, elastic net penalized logistic regression, and generalized logistic regression. Performance was compared utilizing discrimination, calibration, decision curve analysis, and the Brier score. Results: A total of 736 LEMSs resulting in lost playing time occurred among 2103 athletes. Important variables for predicting LEMS included previous number of lower extremity injuries; age; recent history of injuries to the ankle, hamstring, or groin; and recent history of concussion as well as 3-point attempt rate and free throw attempt rate. The XGBoost machine achieved the best performance based on discrimination assessed via internal validation (area under the receiver operating characteristic curve, 0.840), calibration, and decision curve analysis. Conclusion: Machine learning algorithms such as XGBoost outperformed logistic regression in the prediction of a LEMS that will result in lost time. Several variables increased the risk of LEMS, including a history of various lower extremity injuries, recent concussion, and total number of previous injuries.

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Section: Discussionmentioning

confidence: 99%

Section: Detailed Machine-learning Modeling Workflowmentioning

confidence: 99%

Machine Learning for Predicting Lower Extremity Muscle Strain in National Basketball Association Athletes

Pareek

Lavoie-Gagne

et al. 2022

Orthopaedic Journal of Sports Medicine

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“…In the elite rugby, both of males' and females' weight have stopped being increased, but in female league rugby, more than 14 head impact collisions per game (total) and dangerous situations are always lurking 20 . Rugby players with history of concussions are more than twice as likely to suffer muscle and skeletal injuries in the lower limbs 21 . Does concussion have a negative effect on the exertion of safety skills?…”

Section: Discussionmentioning

confidence: 99%

Clarifying the structure of repeated serious injuries on female rugby players

Iwabuchi²,

Watanabe

et al. 2022

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The aim of this study was to grasp the injury situation of Japanese female rugby players in 2020-2021 by network analysis with psychological condition under COVID19 (June 1-30, 2021). The number of respondents was 302. 60% reported serious injuries. The parts were ankle, knee, shoulder, head, etc. Symptoms are ligament / bone / muscle damage or concussion. The caused plays were tackle/tackled, running with no physical contact, etc. There were 39 head injuries. Some ones played while continuing long-term treatment such as knees and ankles. Approximate 40 % of players injured once were injured twice again, and approximate 40% of players injured twice were injured a third time. 30% of all respondents had some chronic symptoms. Network analysis revealed a structure with multiple chronic symptoms (head, shoulders, knees, ankles), a structure of multiple parts of injuries in a single play, and a structure in which the same parts were repeatedly injured in a year. Some players suffered concussions after knee injuries. It suggested the possibility that the physical safety management skills to protect the head might be inferior due to insufficient recovery of knee function as a negative injury chain structure.

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“…These studies included participants from rugby union (n=9, 56% of 16 studies), rugby sevens (n=1, 6%), a combination of union, sevens and league (n=1, 6%), or the code was not reported (n=5, 31%). The cohorts used in these studies varied widely and included amateur/ community, [86][87][88] high school, 89 rugby schools, 90 club, 91 university/college, [92][93][94][95] international, 84 or a combination of levels. [96][97][98][99][100] Many included studies aimed to determine the relationship between injury and potential risk factors.…”

Section: Epidemiologymentioning

confidence: 99%

“…84 87-89 91 94 97 These studies provided Sport Concussion Assessment Tool reference values 89 and determined whether limits should differ between sexes, 84 evaluated standardised assessment and management of concussion, 97 investigated the use of the King-Devick Assessment Tool for a sideline concussion assessment, 87 investigated the neurological deficits that accompany head impacts, 94 explored concussion history on head control, 91 and determined the association between concussion and lower limb injury. 88 From a concussive symptom standpoint, more symptoms and symptom severity were reported in women's rugby union players compared with men's players. 84 In high school rugby union, the median symptom severity was also found to be highest in women with concussion history and lowest in men with no concussion history.…”

Section: Epidemiologymentioning

confidence: 99%

Applied sports science and sports medicine in women’s rugby: systematic scoping review and Delphi study to establish future research priorities

Heyward

Emmonds

Roe

et al. 2022

BMJ Open Sport Exerc Med

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ObjectivesIn part 1, the objective was to undertake a systematic scoping review of applied sports science and sports medicine in women’s rugby, and in part 2 to develop a consensus statement on future research priorities.DesignIn part 1, a systematic search of PubMed (MEDLINE), Scopus and SPORTDiscus (EBSCOhost) was undertaken from the earliest records to January 2021. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020, the PRISMA extension for Scoping Reviews, and the PRISMA extension protocols were followed. In part 2, 31 international experts in women’s rugby (ie, elite players, sports scientists, medical clinicians, sports administrators) participated in a three-round Delphi consensus method. These experts reviewed the findings from part 1 and subsequently provided a list of priority research topics in women’s rugby. Research topics were grouped into expert-based themes and expert-based subthemes via content analysis. Expert-based themes and expert-based subthemes were ranked from very low to very high research priority on a 1–5 Likert scale. Consensus was defined by ≥70% agreement. The median research priority agreement and IQR were calculated for each expert-based theme and subtheme.Data sourcesPubMed (MEDLINE), Scopus and SPORTDiscus (EBSCOhost).Eligibility criteria for selecting studiesStudies were eligible for inclusion if they investigated applied sports science or sports medicine in women’s rugby.ResultsIn part 1, the systematic scoping review identified 123 studies, which were categorised into six sports science and sports medicine evidence-based themes: injury (n=48), physical performance (n=32), match characteristics (n=26), fatigue and recovery (n=6), nutrition (n=6), and psychology (n=5). In part 2, the Delphi method resulted in three expert-based themes achieving consensus on future research priority in women’s rugby: injury (5.0 (1.0)), female health (4.0 (1.0)) and physical performance (4.0 (1.0)).Summary/ConclusionThis two-part systematic scoping review and Delphi consensus is the first study to summarise the applied sports science and sports medicine evidence base in women’s rugby and establish future research priorities. The summary tables from part 1 provide valuable reference information for researchers and practitioners. The three expert-based themes that achieved consensus in part 2 (injury, female health and physical performance) provide clear direction and guidance on future research priorities in women’s rugby. The findings of this two-part study facilitate efficient and coordinated use of scientific resources towards high-priority research themes relevant to a wide range of stakeholders in women’s rugby.

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Diagnosed concussion is associated with increased risk for lower extremity injury in community rugby players

Cited by 18 publications

References 24 publications

Machine Learning for Predicting Lower Extremity Muscle Strain in National Basketball Association Athletes

Machine Learning for Predicting Lower Extremity Muscle Strain in National Basketball Association Athletes

Clarifying the structure of repeated serious injuries on female rugby players

Applied sports science and sports medicine in women’s rugby: systematic scoping review and Delphi study to establish future research priorities

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