Influence of Size and Maturity on Injury in Young Elite Soccer Players

Abstract

The involvement of pre-adolescents in soccer is becoming more and more frequent, and this growing participation generates some concerns about the potential factors for sports injuries. The purpose of this study was to investigate sports injuries in younger (U9–U11) and older (U12–U13) children playing soccer at an elite level, analyzing potential anthropometric and maturity risk factors. A total of 88 elite soccer players aged 9–13 years were investigated. Weight, stature, and sitting height were measured at the start and at the end of the competitive season, computing the relative growth velocities. Additional body composition parameters were taken during a second survey. Maturity offset was calculated using predictive equations based on anthropometric traits such as years from age at peak height velocity (YPHV). Injuries suffered during the competitive season were recorded. Maturity and some anthropometric characteristics were significantly different according to the presence or absence of injuries among the players. Multiple logistic regression revealed that YPHV, body mass index (BMI), and calf muscle area were the factors most significantly correlated with injuries. Players with increased BMI, with decreased calf muscle area, and who were closer to their peak height velocity, were at a higher risk of injury. Findings showed that a monitoring program of anthropometric characteristics taking into account the maturational stage needs to be developed to prevent injuries.

Growth, maturation and injuries in high-level youth football (soccer): A mini review

“…Injury severity is commonly calculated as the number of days elapsed from the day of injury until the day the player returns to full training and/or is available for match selection ( 64 ), often presented as the proportion of injuries falling within defined bins (e.g., percentage of all injuries lasting 7–28 days). Although cut-offs vary slightly between studies and the choice of injury definition affects distributions, the combined findings suggest that 38% (7–74%) of injuries in boys last less than a week, another 38% (16–67%) last between a week and a month, while every fifth injury (21%, 2–37%) lasts more than a month ( 18 , 20 , 21 , 23 , 25 , 28 , 29 , 31 – 37 , 39 , 41 , 46 , 48 , 49 , 53 – 56 , 61 ). For girls, a larger proportion of “mild” injuries is observed, with around a half (51%, 38–52%) lasting a week or less, a third (36%, 32–41%) between a week and a month, and the remaining 16% (12–20%) more than a month ( 55 , 58 , 60 ).…”

“…In general, injury rates are not the same across age groups; however, the age-related pattern is not unified based on the available research. Several studies indicate that rates increase with age ( 24 , 25 , 28 , 31 , 34 , 35 , 41 , 54 ), although others report less clear patterns or bell-shaped relationships peaking around the U15-16 groups ( 20 , 23 , 26 , 27 , 29 , 30 , 37 , 39 , 48 , 53 ). Injury severity and burden also appears to be influenced by age, often peaking in the U14-16 age groups ( 21 , 24 , 35 , 37 , 53 ).…”

Physical Activity for Health and Wellness

“…In addition, the authors used the receiver operating characteristic curve to assess classification performance, achieving satisfactory results. The fourth study by Rinaldo et al [10] departs from the previous themes to deal with injuries that can occur in sporting activities, focusing on the relationship between anthropometric traits and injury occurrence. Their findings pointed out that an increased body mass index, decreased calf muscle area, and being closer to the age of peak height velocity are significant risk factors for injuries in elite soccer players aged 9-13 years.…”

Automatic quadriceps and patellae segmentation of MRI with cascaded U2‐Net and SASSNet deep learning model

“…Thus, autosegmentation is not available to explore how muscle physiology changes 9,10 with development nor how disease affects the developing musculoskeletal system (e.g., cerebral palsy, 11,12 brachial plexus palsy, 13 scoliosis, 14 etc.). Studies exploring the ever-changing 3D pediatric musculoskeletal morphology 15 remain challenging and pediatric muscle volume is often approximated using surrogates (e.g., muscle cross-sectional area 16 ). When full volumes of individual muscles are quantified, the scope has been limited to small databases, 9,11,12,14,15 examining total limb or muscle group volume, 9,15 and typically to a single muscle.…”