Epidemiology of quadriceps muscle strain injuries in elite male Australian football players.

OBJECTIVE: To describe the epidemiology of quadriceps muscle strain injury (QMSI) in elite Australian Football League (AFL) players, explore recovery milestones and determine whether recovery is impacted by factors such as injury type (index vs. re-injury), the primary muscle injured and the mechanism of injury. MEASURES: All QMSI data reported to the Soft Tissue Injury Registry of the AFL from the 2014 to 2020 seasons were evaluated. Player demographic data, circumstances of injury, MRI reports and recovery outcomes following injury were extracted. Descriptive statistics and frequency distributions are presented. Recovery outcomes for injury type, primary muscle injured and the mechanism of injury were compared using univariate analyses. RESULTS: There were 164 QMSIs from 122 players reported (134 index; 30 re-injuries). Almost all (91.3%) QMSIs involved the rectus femoris. Half (48.4%) of the QMSIs occurred during kicking and most commonly affected the dominant kicking leg (72%). The majority occurred at training (64.6%). All re-injuries involved the rectus femoris, most occurred from kicking (63.0%) and within 6 months of the preceding injury (70%). The mean return to play (RTP) time was 25.4 days (95%CI = 22.6-28.2) and rectus femoris injuries took around 14 days longer to RTP than vastii injuries (p = 0.001). QMSIs with a kicking mechanism took the longest to RTP of all injury mechanisms. CONCLUSION: In AFL players, QMSIs occur mostly in the dominant leg from a kicking mechanism. Rectus femoris injuries are more prevalent and result in longer RTP time frames. Re-injuries exclusively involved the rectus femoris, primarily from kicking.

Risk Factors for Quadriceps Muscle Strain Injuries in Sport: A Systematic Review.

OBJECTIVE: To identify risk factors for quadriceps muscle strain injury in sport. DESIGN: Risk factor systematic review. LITERATURE SEARCH: A systematic search was conducted in the MEDLINECINAHL, Embase, AMED, AUSPORT, SPORTDiscus, PEDro, and Cochrane Library databases (from inception to September 2021). STUDY SELECTION CRITERIA: Studies reporting prospective data to evaluate risk factors related to index and/or recurrent quadriceps muscle strain injury. DATA SYNTHESIS: A risk-of-bias assessment (using a modified Quality in Prognosis Studies tool) was performed, and we used best-evidence synthesis to qualitatively synthesize the data to quantify relationships between risk factors and quadriceps muscle injury. RESULTS: Sixteen studies were included, capturing 2408 quadriceps injuries in 11 719 athletes. Meta-analyses were not performed due to clinical heterogeneity. The dominant kicking leg (over 3154 individuals, 1055 injuries), a previous history of quadriceps muscle injury (6208 individuals, 975 injuries), and a recent history of hamstring strain (4087 individuals, 581 injuries) were intrinsic factors associated with quadriceps injury. Extrinsic factors relating to the preseason period and competitive match play increased quadriceps injury risk; participating at higher levels of competition decreased quadriceps injury risk. Age, weight, and flexibility (intrinsic factors) had no association with quadriceps injury. CONCLUSION: Previous quadriceps injury, recent hamstring injury, the dominant kicking leg, and competitive match play were the strongest risk factors for future quadriceps muscle injury in sport. J Orthop Sports Phys Ther 2022;52(6):389-400. doi:10.2519/jospt.2022.10870.

Factors that Impact Self-reported Wellness Scores in Elite Australian Footballers.

INTRODUCTION: This study aimed to 1) identify the impact of external load variables on changes in wellness and 2) identify the impact of age, training/playing history, strength levels, and preseason loads on changes in wellness in elite Australian footballers. METHODS: Data were collected from one team (45 athletes) during the 2017 season. Self-reported wellness was collected daily (4, best score possible; 28, worst score possible). External load/session availability variables were calculated using global positioning systems and session availability data from every training session and match. Additional variables included demographic data, preseason external loads, and strength/power measures. Linear mixed models were built and compared using root mean square error (RMSE) to determine the impact of variables on wellness. RESULTS: The external load variables explained wellness to a large degree (RMSE = 1.55, 95% confidence intervals = 1.52 to 1.57). Modeling athlete ID as a random effect appeared to have the largest impact on wellness, improving the RMSE by 1.06 points. Aside from athlete ID, the variable that had the largest (albeit negligible) impact on wellness was sprint distance covered across preseason. Every additional 2.1 km covered across preseason worsened athletes' in-season wellness scores by 1.2 points (95% confidence intervals = 0.0-2.3). CONCLUSIONS: The isolated impact of the individual variables on wellness was negligible. However, after accounting for the individual athlete variability, the external load variables examined collectively were able to explain wellness to a large extent. These results validate the sensitivity of wellness to monitor individual athletes' responses to the external loads imposed on them.

Session Availability as a Result of Prior Injury Impacts the Risk of Subsequent Non-contact Lower Limb Injury in Elite Male Australian Footballers.

Prior injury is a commonly identified risk factor for subsequent injury. However, a binary approach to classifying prior injury (i.e., yes/no) is commonly implemented and may constrain scientific findings, as it is possible that variations in the amount of time lost due to an injury will impact subsequent injury risk to differing degrees. Accordingly, this study investigated whether session availability, a surrogate marker of prior injury, influenced the risk of subsequent non-contact lower limb injury in Australian footballers. Data were collected from 62 male elite Australian footballers throughout the 2015, 2016, and 2017 Australian Football League seasons. Each athlete's participation status (i.e., full or missed/modified) and any injuries that occurred during training sessions/matches were recorded. As the focus of the current study was prior injury, any training sessions/matches that were missed due to reasons other than an injury (e.g., load management, illness and personal reasons) were removed from the data prior to all analyses. For every Monday during the in-season periods, session availability (%) in the prior 7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, and 84 days was determined as the number of training sessions/matches fully completed (injury free) relative to the number of training sessions/matches possible in each window. Each variable was modeled using logistic regression to determine its impact on subsequent injury risk. Throughout the study period, 173 non-contact lower limb injuries that resulted in at least one missed/modified training session or match during the in-season periods occurred. Greater availability in the prior 7 days increased injury probabilities by up to 4.4%. The impact of session availability on subsequent injury risk diminished with expanding windows (i.e., availability in the prior 14 days through to the prior 84 days). Lesser availability in the prior 84 days increased injury probabilities by up to 14.1%, only when coupled with greater availability in the prior 7 days. Session availability may provide an informative marker of the impact of prior injury on subsequent injury risk and can be used by coaches and clinicians to guide the progression of training, particularly for athletes that are returning from long periods of injury.