High exposure to game conditions was the dominant injury risk factor for collegiate football players, but a surprisingly mild degree of low back dysfunction and poor core-muscle endurance appeared to be important modifiable risk factors that should be identified and addressed before participation.

Sport injuries restrict participation, impose a substantial economic burden, and can have persisting adverse effects on health-related quality of life. The effective use of Internet of Things (IoT), when combined with analytics approaches, can improve player safety through identification of injury risk factors that can be addressed by targeted risk reduction training activities. Use of IoT devices can facilitate highly efficient quantification of relevant functional capabilities prior to sport participation, which could substantially advance the prevailing sport injury management paradigm. This study introduces a framework for using sensor-derived IoT data to supplement other data for objective estimation of each individual college football player's level of injury risk, which is an approach to injury prevention that has not been previously reported. A cohort of 45 NCAA Division I-FCS college players provided data in the form of self-ratings of persisting effects of previous injuries and single-leg postural stability test. Instantaneous change in body mass acceleration (jerk) during the test was quantified by a smartphone accelerometer, with data wirelessly transmitted to a secure cloud server. Injuries sustained from the beginning of practice sessions until the end of the 13-game season were documented, along with the number of games played by each athlete over the course of a 13-game season. Results demonstrate a strong prediction model. Our approach may have strong relevance to the estimation of injury risk for other physically demanding activities. Clearly, there is great potential for improvement of injury prevention initiatives through identification of individual athletes who possess elevated injury risk and targeted interventions.

Wilkerson, GB, Gupta, A, Allen, JR, Keith, CM, and Colston, MA. Utilization of practice session average inertial load to quantify college football injury risk. J Strength Cond Res 30(9): 2369-2374, 2016-Relatively few studies have investigated the potential injury prevention value of data derived from recently developed wearable technology for measurement of body mass accelerations during the performance of sport-related activities. The available evidence has been derived from studies focused on avoidance of overtraining syndrome, which is believed to induce a chronically fatigued state that can be identified through monitoring of inertial load accumulation. Reduced variability in movement patterns is also believed to be an important injury risk factor, but no evidence currently exists to guide interpretation of data derived from inertial measurement units (IMUs) in this regard. We retrospectively analyzed archived data for a cohort of 45 National Collegiate Athletic Association Division 1-football bowl subdivision football players who wore IMUs on the upper back during practice sessions to quantify any associations between average inertial load measured during practice sessions and occurrence of musculoskeletal sprains and strains. Both the coefficient of variation for average inertial load and frequent exposure to game conditions were found to be strongly associated with injury occurrence. Having either or both of the 2 risk factors provided strong discrimination between injured and noninjured players (χ = 9.048; p = 0.004; odds ratio = 8.04; 90% CI: 2.39, 27.03). Our findings may facilitate identification of individual football players who are likely to derive the greatest benefit from training activities designed to reduce injury risk through improved adaptability to rapidly changing environmental demands.

No abstract

Self-reported effects of previous injury may be one method to efficiently identify athletes who possess elevated injury risk, and subsequently deliver preventive interventions, thereby providing an alternative method to time-intensive functional testing.