Non-time-loss injuries accounted for nearly half of the injuries in men's and women's soccer. Sex differences were found in competition injuries, specifically for concussion. Further study into the incidence, treatment and outcome of non-time-loss injuries may identify a more accurate burden of these injuries.

academic years. Main Outcome Measure(s): Injury data from the NCAA Injury Surveillance Program were analyzed. A severe injury (1) occurred during a sanctioned competition or practice, (2) required medical attention by an athletic trainer or physician, and (3) resulted in at least 21 days lost from sport activity or a premature end to the sport season. Injury counts, proportions, rates per 1000 athlete-exposures (AEs), rate ratios (RRs), and injury proportion ratios were reported with 95% confidence intervals (CIs).Results: A total of 3183 severe injuries were reported, for an injury rate of 0.66/1000 AEs. Wrestling had the highest severe injury rate (1.73/1000 AEs), followed by women's gymnastics (1.40/1000 AEs) and football (0.97/1000 AEs). Overall, the severe injury rate was higher in competition than in practice (RR ¼ 4.25, 95% CI ¼ 3.97, 4.56). Most severe injuries were reported during the regular season (69.3%, n ¼ 2206); however, severe injury rates did not differ between the preseason and regular season (RR ¼ 0.98, 95% CI ¼ 0.91, 1.06). Common severely injured body parts were the knee (32.9%, n ¼ 1047), lower leg/ankle/foot (22.5%, n ¼ 715), and head/face/ neck (11.2%, n ¼ 358). Common severe injury diagnoses were sprains (32.9%, n ¼ 1048), strains (16.9%, n ¼ 538), and fractures (14.4%, n ¼ 458). Common severe injury mechanisms were player contact (39.3%, n ¼ 1251), noncontact (25.1%, n ¼ 800), and surface contact (12.0%, n ¼ 383).Conclusions: Severe injuries occurred across many sports and by numerous mechanisms. By identifying these sportspecific patterns, clinicians' efforts can be tailored toward improving injury-prevention strategies and health outcomes.Key Words: injury surveillance, injury rates, injury prevention Key PointsSevere injury rates varied by sport, event type, and sex. As patterns within specific sports are identified, health care providers can develop injury-prevention strategies and promote efforts such as rule changes to improve athlete safety.

Background:Noncontact anterior cruciate ligament (ACL) injury in adolescent female athletes is an increasing problem. The knee-ankle separation ratio (KASR), calculated at initial contact (IC) and peak flexion (PF) during the drop vertical jump (DVJ), is a measure of dynamic knee valgus. The Microsoft Kinect V2 has shown promise as a reliable and valid marker-less motion capture device.Hypothesis:The Kinect V2 will demonstrate good to excellent correlation between KASR results at IC and PF during the DVJ, as compared with a “gold standard” Vicon motion analysis system.Study Design:Descriptive laboratory study.Level of Evidence:Level 2.Methods:Thirty-eight healthy volunteer subjects (20 male, 18 female) performed 5 DVJ trials, simultaneously measured by a Vicon MX-T40S system, 2 AMTI force platforms, and a Kinect V2 with customized software. A total of 190 jumps were completed. The KASR was calculated at IC and PF during the DVJ. The intraclass correlation coefficient (ICC) assessed the degree of KASR agreement between the Kinect and Vicon systems.Results:The ICCs of the Kinect V2 and Vicon KASR at IC and PF were 0.84 and 0.95, respectively, showing excellent agreement between the 2 measures. The Kinect V2 successfully identified the KASR at PF and IC frames in 182 of 190 trials, demonstrating 95.8% reliability.Conclusion:The Kinect V2 demonstrated excellent ICC of the KASR at IC and PF during the DVJ when compared with the Vicon system. A customized Kinect V2 software program demonstrated good reliability in identifying the KASR at IC and PF during the DVJ.Clinical Relevance:Reliable, valid, inexpensive, and efficient screening tools may improve the accessibility of motion analysis assessment of adolescent female athletes.

A study was conducted to evaluate the use of the skeletal model generated by the Microsoft Kinect SDK in capturing four biomechanical measures during the Drop Vertical Jump test. These measures, which include: knee valgus motion from initial contact to peak flexion, frontal plane knee angle at initial contact, frontal plane knee angle at peak flexion, and knee-to-ankle separation ratio at peak flexion, have proven to be useful in screening for future knee anterior cruciate ligament (ACL) injuries among female athletes. A marker-based Vicon motion capture system was used for ground truth. Results indicate that the Kinect skeletal model likely has acceptable accuracy for use as part of a screening tool to identify elevated risk for ACL injury.

Context Early single-sport specialization and the relative age effect are often cited as improving the chances of sport success. Both concepts suggest that genetics and the environment have little influence on sport success. Objective To compare National Collegiate Athletic Association Division I student-athletes (SAs) with their undergraduate nonathlete peers (NAs) in terms of birth month, age of sport initiation, and age of single-sport specialization. A family history of sport participation was examined as a potential marker for genetic and social influences. Design Cross-sectional survey. Setting Large urban university. Patients or Other Participants A total of 273 Division I SAs (138 women, 135 men) and 155 NAs (78 women, 77 men) participated. The NAs had been involved in competitive youth sports before entering the university. Main Outcome Measure(s) Participants were asked to complete a questionnaire that addressed the age of sport initiation, birth month, age of single-sport specialization, and parental and sibling sport achievement. Main Results Neither birth month nor the age of sport initiation differed between groups (age of sport initiation = 7.16 ± 2.6 years for the SAs versus 7.71 ± 3.5 for the NAs; P = .176). A larger proportion of SAs began participating before 10 years of age (80% versus 63%; P = .02). The parents of SAs were more likely to have participated in collegiate (32.4% versus 8.4%; P < .0001) and professional (10.9% versus 1.3%; P = .0005) sports. The SAs specialized in a single sport at an older age (15.38 ± 2.7 years versus 14.30 ± 2.6 years; P = .002). Both groups participated in multiple sports in childhood (SAs = 3.9 ± 1.8 sports, NAs = 3.2 ± 1.8 sports; P = .366). Conclusions The Division I SAs did not specialize in a single sport at a younger age than the NAs. No evidence of a relative age effect was present. Importantly, higher levels of sport achievement among the parents and siblings of SAs suggest that genetic endowment and family or other environmental dynamics play a large role in athletic performance. Overall, the results are not consistent with deliberate practice theory and point toward an alternative model that includes not only sport-specific skill development but also genetic and social factors as key elements of long-term sport achievement.