Search Results (3)

Background: Muscle injuries pose a significant challenge in sports, leading to decreased performance and shortened career longevity. Individuals homozygous for the nonsense X allele of the ACTN3 rs1815739 (R577X) polymorphism, characterized by a complete absence of α-actinin-3, have been associated with reduced power performance and may have an increased injury risk. This study aimed to investigate the association between the ACTN3 R577X polymorphism and both volleyball player status and the risk of non-contact musculoskeletal injuries in female volleyball players. Methods: The study included 5382 Turkish and Russian subjects of European descent (187 professional volleyball players and 5195 controls), of whom 50 female players provided injury data. Sport-related injury information was obtained from medical records maintained by team physicians and physiotherapists. Results: A pooled analysis of the two cohorts demonstrated that the frequency of the ACTN3 X allele was significantly lower in volleyball players than in controls, with an odds ratio of 0.763 (95% CI: 0.61–0.95, p = 0.02). In the pre-specified recessive contrast (XX vs. RR + RX) among 50 players, exact methods indicated higher injury odds for the XX genotype (OR = 7.87, 95% CI: 0.94–374.58; p = 0.0366), which was classified as borderline/exploratory. Penalized (Firth) regression produced estimates of a similar magnitude after adjustment for age and playing position (adjusted OR = 5.92, 95% CI: 1.12–60.98), although confidence intervals remained wide. Conclusions: The ACTN3 X allele is underrepresented in professional volleyball players, and it is associated with an increased risk of muscle injury in female players. Full article

Football clubs regularly test and monitor players, with different approaches reflecting player age and competitive level. This narrative review aims to summarise justifications for testing and commonly used testing protocols. We also aim to discuss the validity and reliability of specific tests used to assess football players and provide a holistic overview of protocols currently used in football or those demonstrating potential utility. The PubMed, SportDiscus, and Google Scholar databases were screened for relevant articles from inception to September 2024. Articles that met our inclusion criteria documented tests for several purposes, including talent identification or the assessment of growth/maturation, physiological capacity, sport-specific skill, health status, monitoring fatigue/recovery, training adaptation, and injury risk factors. We provide information on specific tests of anthropometry, physical capacity, biochemical markers, psychological indices, injury risk screening, sport-specific skills, and genetic profile and highlight where certain tests may require further evidence to support their use. The available evidence suggests that test selection and implementation are influenced by financial resources, coach perceptions, and playing schedules. The ability to conduct field-based testing at low cost and to test multiple players simultaneously appear to be key drivers of test development and implementation among practitioners working in elite football environments. Full article

Talent selection programmes choose athletes for talent development pathways. Currently, the set of psychosocial variables that determine talent selection in youth Rugby Union are unknown, with the literature almost exclusively focusing on physiological variables. The purpose of this study was to use a novel machine learning approach to identify the physiological and psychosocial models that predict selection to a regional age-grade rugby union team. Age-grade club rugby players (n = 104; age, 15.47 ± 0.80; U16, n = 62; U18, n = 42) were assessed for physiological and psychosocial factors during regional talent selection days. Predictive models (selected vs. non-selected) were created for forwards, backs, and across all players using Bayesian machine learning. The generated physiological models correctly classified 67.55% of all players, 70.09% of forwards, and 62.50% of backs. Greater hand-grip strength, faster 10 m and 40 m sprint, and power were common features for selection. The generated psychosocial models correctly classified 62.26% of all players, 73.66% of forwards, and 60.42% of backs. Reduced burnout, reduced emotional exhaustion, and lower reduced sense of accomplishment, were common features for selection. Selection appears to be predominantly based on greater strength, speed, and power, as well as lower athlete burnout. Full article