Predictive Modeling of Hamstring Strain Injuries in Elite Australian Footballers

Recalibrating the risk of hamstring strain injury (HSI): A 2020 systematic review and meta-analysis of risk factors for index and recurrent hamstring strain injury in sport

OBJECTIVE

To systematically review risk factors for hamstring strain injury (HSI).

DESIGN

Systematic review update.

DATA SOURCES

Database searches: (1) inception to 2011 (original), and (2) 2011 to December 2018 (update). Citation tracking, manual reference and ahead of press searches.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

Studies presenting prospective data evaluating factors associated with the risk of index and/or recurrent HSI.

METHOD

Search result screening and risk of bias assessment. A best evidence synthesis for each factor and meta-analysis, where possible, to determine the association with risk of HSI.

RESULTS

The 78 studies captured 8,319 total HSIs, including 967 recurrences, in 71,324 athletes. Older age (standardised mean difference=1.6, p=0.002), any history of HSI (risk ratio (RR)=2.7, p<0.001), a recent HSI (RR=4.8, p<0.001), previous anterior cruciate ligament (ACL) injury (RR=1.7, p=0.002) and previous calf strain injury (RR=1.5, p<0.001) were significant risk factors for HSI. From the best evidence synthesis, factors relating to sports performance and match play, running and hamstring strength were most consistently associated with HSI risk. The risk of recurrent HSI is best evaluated using clinical data and not the MRI characteristics of the index injury.

SUMMARY/CONCLUSION

Older age and a history of HSI are the strongest risk factors for HSI. Future research may be directed towards exploring the interaction of risk factors and how these relationships fluctuate over time given the occurrence of index and recurrent HSI in sport is multifactorial.

Machine Learning in Modeling High School Sport Concussion Symptom Resolve

INTRODUCTION

Concussion prevalence in sport is well recognized, so too is the challenge of clinical and return-to-play management for an injury with an inherent indeterminant time course of resolve. A clear, valid insight into the anticipated resolution time could assist in planning treatment intervention.

PURPOSE

This study implemented a supervised machine learning-based approach in modeling estimated symptom resolve time in high school athletes who incurred a concussion during sport activity.

METHODS

We examined the efficacy of 10 classification algorithms using machine learning for the prediction of symptom resolution time (within 7, 14, or 28 d), with a data set representing 3 yr of concussions suffered by high school student-athletes in football (most concussion incidents) and other contact sports.

RESULTS

The most prevalent sport-related concussion reported symptom was headache (94.9%), followed by dizziness (74.3%) and difficulty concentrating (61.1%). For all three category thresholds of predicted symptom resolution time, single-factor ANOVA revealed statistically significant performance differences across the 10 classification models for all learners at a 95% confidence interval (P = 0.000). Naïve Bayes and Random Forest with either 100 or 500 trees were the top-performing learners with an area under the receiver operating characteristic curve performance ranging between 0.656 and 0.742 (0.0-1.0 scale).

CONCLUSIONS

Considering the limitations of these data specific to symptom presentation and resolve, supervised machine learning demonstrated efficacy, while warranting further exploration, in developing symptom-based prediction models for practical estimation of sport-related concussion recovery in enhancing clinical decision support.

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.

Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review

Background

The application of artificial intelligence (AI) opens an interesting perspective for predicting injury risk and performance in team sports. A better understanding of the techniques of AI employed and of the sports that are using AI is clearly warranted. The purpose of this study is to identify which AI approaches have been applied to investigate sport performance and injury risk and to find out which AI techniques each sport has been using.

Methods

Systematic searches through the PubMed, Scopus, and Web of Science online databases were conducted for articles reporting AI techniques or methods applied to team sports athletes.

Results

Fifty-eight studies were included in the review with 11 AI techniques or methods being applied in 12 team sports. Pooled sample consisted of 6456 participants (97% male, 25 ± 8 years old; 3% female, 21 ± 10 years old) with 76% of them being professional athletes. The AI techniques or methods most frequently used were artificial neural networks, decision tree classifier, support vector machine, and Markov process with good performance metrics for all of them. Soccer, basketball, handball, and volleyball were the team sports with more applications of AI.

Conclusions

The results of this review suggest a prevalent application of AI methods in team sports based on the number of published studies. The current state of development in the area proposes a promising future with regard to AI use in team sports. Further evaluation research based on prospective methods is warranted to establish the predictive performance of specific AI techniques and methods.

Electronic supplementary material

The online version of this article (10.1186/s40798-019-0202-3) contains supplementary material, which is available to authorized users.

Session Availability as a Result of Prior Injury Impacts the Risk of Subsequent Non-contact Lower Limb 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.