CrossFit has gained recognition and interest among physically active populations being one of the most popular and rapidly growing exercise regimens worldwide. Due to the intense and repetitive nature of CrossFit, concerns have been raised over the potential injury risks that are associated with its training including rhabdomyolysis and musculoskeletal injuries. However, identification of risk factors for predicting injuries in CrossFit athletes has been limited by the absence of relevant big epidemiological studies. The main purpose of this paper is the identification of risk factors and the development of machine learning-based models using ensemble learning that can predict CrossFit injuries. To accomplish the aforementioned targets, a survey-based epidemiological study was conducted in Greece to collect data on musculoskeletal injuries in CrossFit practitioners. A Machine Learning (ML) pipeline was then implemented that involved data pre-processing, feature selection and well-known ML models. The performance of the proposed ML models was assessed using a comprehensive cross validation mechanism whereas a discussion on the nature of the selected features is also provided. An area under the curve (AUC) of 77.93% was achieved by the best ML model using ensemble learning (Adaboost) on the group of six selected risk factors. The effectiveness of the proposed approach was evaluated in a comparative analysis with respect to numerous performance metrics including accuracy, sensitivity, specificity, AUC and confusion matrices to confirm its clinical relevance. The results are the basis for the development of reliable tools for the prediction of injuries in CrossFit.

The purpose of this study was to determine the extent to which the mean peak moment (MPM) of knee flexors and extensors could predict performance in a group of healthy individuals. Eighty-four healthy individuals-32 men and 52 women (mean age, 22.1 ± 3 years; range, 18-35 years)-participated in this study. Isokinetic unilateral concentric knee flexor and extensor MPM was assessed isokinetically at angular velocities of 60°/s and 180°/s. Functional performance was assessed using the single hop of distance (SHD). Positive moderate to good statistically significant correlations (r = .636 to r = .673) were found between knee flexor and extensor MPM at 60°/s and 180°/s for the SHD test. Knee flexor and extensor MPMs are strong predictors for the SHD test at 60°/s and 180°/s (R² = .40 to R² = .45). Knee flexor and extensor strength was substantially correlated with SHD.

BACKGROUND: Both isokinetic testing and functional tests are commonly used during the rehabilitation programme of individuals. Limited information exists in the literature, regarding the correlation between these two testing methods. OBJECTIVE: To de

CrossFit (CF) is a popular and rapidly expanding training program in Greece and worldwide. However, there is a lack of scientific evidence on the risk of musculoskeletal injuries related to CF in the Greek population. A self-administered survey of 1224 Greek CF practitioners aged 18 to 59 was conducted and analyzed using the Statistical Package for Social Sciences (SPSS) software. The highest percentage of the participants (34%) practiced 5 days per week for 60 min (42.2%) and had 2 days per week of rest (41.7%). A total of 273 individuals (23%) participated in CF competitions and 948 (77%) did not. The results showed that the most common injuries were muscle injuries (51.3%), followed by tendinopathies (49.6%) and joint injuries (26.6%). The shoulders (56.6%; n = 303), knees (31.8%; n = 170), and lumbar spine (33.1%; n = 177) were the most commonly injured locations. The logistic regression model showed that participation in competitions (p = 0.001), rest per week (p = 0.01), duration of training per session (p = 0.001), and frequency of training per week (p = 0.03) were statistically significant factors for injury. Training level was not a statistically significant factor for injury (p = 0.43). As CF continues to gain popularity on a global scale and the number of athletes gradually increases, it is important to monitor the safety of practitioners. Clinicians should consider participation in competitions, rest, training duration, and frequency in order to make CF safer.

Background: Football has a high injury risk due to speed and contact, and injury patterns may vary by playing position. Positional roles affect physical and physiological demands and may influence injury characteristics. Although this has been examined in professionals, data from amateur players are scarce. This study examined the incidence, type, and severity of injuries among amateur footballers in Greece with respect to playing position. Methods: A retrospective epidemiological study analyzed musculoskeletal injuries in 222 amateur male football players during the 2022–2023 season. Data were collected via a CHERRIES-compliant online survey (SurveyMonkey^®) from May to July 2023. Eligible participants were active male athletes aged ≥18 years competing in amateur Greek leagues. Injuries were defined according to the FIFA–UEFA consensus and expressed as incidence rates per 1000 h of exposure. Statistical analyses used SPSS v25 with significance at p < 0.05. Results: Among players (mean age: 25.3 ± 5.7 years), injury prevalence ranged from 65.1% (DFs) to 79.3% (GKs) with no significant association between playing position and injury risk (p = 0.379). Injury incidence ranged from 4.5 to 5.7 per 1000 h, highest among MFs. Incidence rates ranged between 1.33 and 2.74 injuries/1000 h in matches versus 1.33 to 2.09/1000 h in training, with DFs, FWs, and MFs more prone to match injuries, whereas GKs had slightly higher training rates; however, the number of injuries did not significantly differ between games and training across positions (χ² = 5.21, p = 0.517). Muscle strains and lower-limb injuries predominated. Injury severity differed significantly by position (p = 0.001), but injury type and mechanism did not. Conclusions: GKs and MFs showed the highest prevalence and incidence, but position was not linked to overall risk. Severity differences highlight the need for position-specific prevention strategies.