Sprinting Biomechanics and Hamstring Injuries: Is There a Link? A Literature Review

Utilising Inertial Measurement Units and Force-Velocity Profiling to Explore the Relationship Between Hamstring Strain Injury and Running Biomechanics

The purpose of this study was to retrospectively and prospectively explore associations between running biomechanics and hamstring strain injury (HSI) using field-based technology. Twenty-three amateur sprinters performed 40 m maximum-effort sprints and then underwent a one-year injury surveillance period. For the first 30 m of acceleration, sprint mechanics were quantified through force-velocity profiling. In the upright phase of the sprint, an inertial measurement unit (IMU) system measured sagittal plane pelvic and hip kinematics at the point of contact (POC), as well as step and stride time. Cross-sectional analysis revealed no differences between participants with a history of HSI and controls except for anterior pelvic tilt (increased pelvic tilt on the injured side compared to controls). Prospectively, two participants sustained HSIs in the surveillance period; thus, the small sample size limited formal statistical analysis. A review of cohort percentiles, however, revealed both participants scored in the higher percentiles for variables associated with a velocity-oriented profile. Overall, this study may be considered a feasibility trial of novel technology, and the preliminary findings present a case for further investigation. Several practical insights are offered to direct future research to ultimately inform HSI prevention strategies.

The machine learning algorithm based on decision tree optimization for pattern recognition in track and field sports

This study aims to solve the problems of insufficient accuracy and low efficiency of the existing methods in sprint pattern recognition to optimize the training and competition strategies of athletes. Firstly, the data collected in this study come from high-precision sensors and computer simulation, involving key biomechanical parameters in sprint, such as step frequency, stride length and acceleration. The dataset covers multiple tests of multiple athletes, ensuring the diversity of samples. Secondly, an optimized machine learning algorithm based on decision tree is adopted. It combines the advantages of Random Forest (RF) and Gradient Boosting Tree (GBT), and improves the accuracy and efficiency of the model in sprint pattern recognition by adaptively adjusting the hyperparameter and tree structure. Specifically, by introducing adaptive feature selection and ensemble learning methods, the decision tree algorithm effectively improves the recognition ability of the model for different athletes and sports states, thus reducing the over-fitting phenomenon and improving the generalization ability. In the process of model training, cross-validation and grid search optimization methods are adopted to ensure the reasonable selection of super parameters. Moreover, the superiority of the model is verified by comparing with the commonly used algorithms such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN). The accuracy rate on the test set is 94.9%, which is higher than that of SVM (87.0%) and CNN (92.0%). In addition, the optimized decision tree algorithm performs well in computational efficiency. However, the training data of this model comes from the simulation environment, which may deviate from the real game data. Future research can verify the generalization ability of the model through more actual data.

Hamstring muscle injuries in athletics

Hamstring muscle injuries (HMI) are a common and recurrent issue in the sport of athletics, particularly in sprinting and jumping disciplines. This review summarizes the latest literature on hamstring muscle injuries in athletics from a clinical perspective. The considerable heterogeneity in injury definitions and reporting methodologies among studies still needs to be addressed for greater clarity. Expert teams have recently developed evidence-based muscle injury classification systems whose application could guide clinical decision-making; however, no system has been adopted universally in clinical practice, yet.The most common risk factor for HMI is a previously sustained injury, particularly early after return-to-sport. Other modifiable (e.g. weakness of thigh muscles, high-speed running exposure) and non-modifiable (e.g. older age) risk factors have limited evidence linking them to injury. Reducing injury may be achieved through exercise-based programs, but their specific components and their practical applicability remain unclear.Post-injury management follows similar recommendations to other soft tissue injuries, with a graded progression through stages of rehabilitation to full return to training and then competition, based on symptoms and clinical signs to guide the individual speed of the recovery journey. Evidence favoring surgical repair is conflicting and limited to specific injury sub-types (e.g. proximal avulsions). Further research is needed on specific rehabilitation components and progression criteria, where more individualized approaches could address the high rates of recurrent HMI. Prognostically, a combination of physical examination and magnetic resonance imaging (MRI) seems superior to imaging alone when predicting 'recovery duration,' particularly at the individual level.

Hamstring muscle injury is preceded by a short period of higher running demands in professional football players

The aim of this study was to examine match running patterns before a hamstring muscle injury occurs during a match in male professional football players. A total of 281 male professional football players belonging to 7 teams from LaLiga were prospectively monitored over three seasons. Among these, 36 players suffered a non-contact hamstring muscle injury during an official match. The injuries were recorded by the medical staff, including the minute when the injury occurred. Running distances at different speed thresholds for 5 min and 15 min before the injury were compared to mean values of the previous 5 matches for the same time points. There were a total of 44 non-contact hamstring muscle injuries, which represents a hamstring muscle injury incidence of 3.34 injuries/1000 h of match exposure. The average time loss for these injuries was 33 ± 28 days (range 7 to 117 days). In the 15 min prior to the injury, players ran a similar distance as in control matches (p from 0.22 to 0.08). However, players ran a greater distance in the 5-min period before the injury than in control matches at 21.0-23.9 km/h (p < 0.001) and at ≥ 24 km/h (p < 0.001). The odds ratio for a hamstring muscle injury was 7.147 for those players who ran > 30.0 m at ≥ 21 km/h in a 5-min period (p < 0.001). Hamstring muscle injuries during competition were preceded by 5 min of higher running demands at > 21 km/h, compared with control matches. This suggests that a short period of unusual running increases the risk of hamstring muscle injury in professional football players.

Resistance Exercise for Improving Running Economy and Running Biomechanics and Decreasing Running-Related Injury Risk: A Narrative Review

It is well-accepted that at least a certain amount of resistance exercise (RE) is recommended for most endurance athletes. In this review, we aim to summarize the evidence regarding the effects of RE on running economy, running biomechanics, and running-related injury risk in endurance runners. The evidence robustly shows that lower limb RE is effective for improving running economy and performance, with a combination of strength and plyometric training being recommended to improve RE. Isometric training is also emerging as a possible alternative to implement during periods of high overall training load. Lower limb RE may change some aspects of joint kinematics during running; however, the evidence regarding the effects on kinetics is limited. Lower limb RE may help reduce running-related injury risk, but further evidence is needed.