Sports injuries and risk factors for table tennis among nonprofessional collegiate athletes: A cross-sectional study

Musculoskeletal Injuries in Table Tennis during Competition: A Systematic Review

The study aimed to investigate the incidence of musculoskeletal injuries among table tennis players and identify their associated risk factors. We conducted a literature search in online databases using relevant search terms related to "table tennis" and "injury". To compare the rate of injuries according to player-related variables, we extracted information on sex, level of games, circumstances of injury, and duration of sport absence after injury. Furthermore, we calculated the odds ratios based on the number of injuries according to variables. Eight articles included 873 professional table tennis players, revealing an overall musculoskeletal injury rate of 3.6% (31 musculoskeletal injuries). The incidence was 10.0 injuries per 1,000 playing hours (range, 0-27.0). The rate of musculoskeletal injuries for female and male players were 3.2% and 3.8%, respectively, and those for Summer Olympic Games, Asian Games, and domestic national games was 4.8%, 15.4%, and 0%, respectively. More than half (52%) of injuries occurred during training, and most injuries (64%) were of a nature that did not cause time loss. This review provides valuable information about the incidence of musculoskeletal injuries in table tennis competitions. However, owing to the limitations of this study in identifying injury-related risk factors, future studies analyzing comprehensive variables are required.

Incidence of injuries and associated risk factors in a sample of Italian recreational padel players

BACKGROUND

Padel is a racket sport similar to tennis, which since the COVID-19 pandemic has been gaining popularity among recreational players. Despite its popularity, epidemiological studies are still lacking. This study aimed to identify the prevalence of injuries among Italian recreational padel players and associated risk factors.

METHODS

A questionnaire was administered to 127 recreational padel players between January 2022 to March 2022. The questionnaire consisted of questions to collect information regarding intrinsic and extrinsic factors related to padel practice, injury location, and injury typology. Frequencies and proportions were calculated for variable description. Binomial logistic regression was adopted to identify potential risk factors.

RESULTS

Of the 127 participants, 100 (78.8%) reported having had an injury during the previous year. Such injury in most cases (37%) resulted in an absence from padel from 8 to 28 days. The most common injury locations were lower leg/Achilles tendon (17.1%) and elbow (13.8%), while the most common injury typologies were tendinopathies (33.6%) and contusions (14.9%). Among the identified risk factors the amount of time played padel (3.5-5 hours/week), racket type (round), and racket weight (either not knowing or 356-375 g) resulted in an increase in the probability of occurring an injury.

CONCLUSIONS

Recreational padel results in a high percentage of injuries. These frequently result in tendinopathies of the Achilles tendon and elbow. Inadequate racket characteristics and the amount of time played could contribute to an increase in injury incidence.

Correlation of Glenohumeral Internal Rotation Deficit With Shoulder Pain in Elite Table Tennis Players

OBJECTIVES

The aims of the study were (1) to investigate glenohumeral internal rotation deficit (a difference in internal rotation of 15.6 degrees or more between dominant and nondominant shoulders) and its correlation with self-reported shoulder pain in table tennis players and (2) to find the optimal cutoff point for the difference in the internal rotation range of motion between dominant and nondominant shoulders of self-reported shoulder pain.

DESIGN

The internal rotation range of motion of both shoulders of 46 table tennis players was measured in the supine and side-lying positions, and the external rotation range of motion was measured in the supine position.

RESULTS

Significant differences existed in internal rotation range of motion between the two sides in the supine ( z = 6.53, P < 0.001) and side-lying positions ( z = 5.67, P < 0.001). Self-reported shoulder pain was associated with glenohumeral internal rotation deficit (odds ratio = 6.86, 95% confidence interval = 1.752-26.832, P = 0.006). The cutoff points for the difference in internal rotation range of motion between the sides of self-reported shoulder pain were 17.9 degrees in the supine position and 11.1 degrees in the side-lying position.

CONCLUSIONS

Table tennis players exhibited glenohumeral internal rotation deficit. There was a correlation between glenohumeral internal rotation deficit and self-reported shoulder pain in the past year; therefore, glenohumeral internal rotation deficit may be a risk factor for shoulder pain in table tennis players.

Machine Leaning-Based Optimization Algorithm for Myocardial Injury under High-Intensity Exercise in Track and Field Athletes

In order to train at high-intensity, athletics can again cause varying degrees of myocardial damage. Evaluating the balance between exercise myocardial injury and exercise intensity should actively prevent myocardial injury caused by high-intensity athletic training. In this paper, an intelligent optimization algorithm is used to investigate the degree of myocardial injury. The basic idea is to define the measured data and the output of the numerical model as an objective function of the structural parameters, to obtain the structural parameters by finding ways to continuously optimize the objective function to be close to the observed values, and to identify the injury based on the changes in these parameters before and after myocardial injury. The objective function can be defined in various ways, and the myocardial injury optimization algorithm can be chosen. In order to obtain the best computational results, numerical simulations of damage identification are performed using the objective function and three machine learning-based optimization algorithms. The computational results show that the combination of the objective function and the machine learning algorithms provides good accuracy and computational speed in identifying myocardial injury.