Injuries Associated with the Practice of Calm Water Kayaking in the Canoeing Modality

The Risk Factors Associated with Grip Lock Injuries in Artistic Gymnasts: A Systematic Review

Artistic gymnastics (AG) is a sport that demands grace, strength, and flexibility, leading to a broad spectrum of injuries. The dowel grip (DG) is widely used by gymnasts to securely hold onto the high bar or uneven bars. However, incorrect usage of the DG can result in grip lock (GL) injuries. This systematic review aims to (1) identify studies that have investigated the risk factors related to GL injuries among gymnasts and (2) synthesize the key evidence. A comprehensive electronic search was conducted in the following databases: PubMed, ScienceDirect, Elsevier, SportDiscus, and Google Scholar, covering the period from their inception until November 2022. The data extraction and analysis were independently completed by two investigators. A total of 90 relevant studies were initially identified, out of which seven clinical trials met the eligibility criteria. For the quantitative synthesis, five studies were included. The details extracted from each article include: the sample characteristics (number, gender, age, and health status), the study design, the instrumentation or intervention used, and the final results. Our results revealed that the underlying causes of the risk factors of GL injuries were the irregular checking of the dowel grip and the mating surface of the bar, the tearing of the dowel of the leather strap, and the use of the dowel grip in different competition apparatuses. In addition, GL injuries may occur either as severe forearm fractures or mild injuries. Excessive flexion of the forearm and overpronation of the wrist during rotational movements, such as the swing or backward/forward giant circle, may increase the possibility of GL injury on the high bar. Future studies should focus on GL injury prevention strategy and rehabilitation protocol for GL injuries. Further high-quality research is required to establish the validity of these findings.

A Narrative Review for a Machine Learning Application in Sports: An Example Based on Injury Forecasting in Soccer

In the last decade, the number of studies about machine learning algorithms applied to sports, e.g., injury forecasting and athlete performance prediction, have rapidly increased. Due to the number of works and experiments already present in the state-of-the-art regarding machine-learning techniques in sport science, the aim of this narrative review is to provide a guideline describing a correct approach for training, validating, and testing machine learning models to predict events in sports science. The main contribution of this narrative review is to highlight any possible strengths and limitations during all the stages of model development, i.e., training, validation, testing, and interpretation, in order to limit possible errors that could induce misleading results. In particular, this paper shows an example about injury forecaster that provides a description of all the features that could be used to predict injuries, all the possible pre-processing approaches for time series analysis, how to correctly split the dataset to train and test the predictive models, and the importance to explain the decision-making approach of the white and black box models.