Background: Hamstring strains remain one of the most prevalent injuries in Australian Rules football. The authors prospectively examined the relationship between musculotendinous stiffness of the hamstring and leg stiffness with hamstring injury in professional Australian Rules footballers during the 2006 season. Hypothesis: Higher hamstring stiffness and leg stiffness are related to noncontact, soft tissue hamstring injury risk in professional Australian Rules footballers. Study Design: Case-control study; Level of evidence, 3. Methods: Unilateral hamstring stiffness and leg stiffness were assessed in 136 professional footballers in the month before the commencement of the competitive season. This information was then investigated relative to players who suffered noncontact, soft tissue hamstring injuries during either matches or training throughout the season to identify whether preseason stiffness was related to injury occurrence. Results: Fourteen tested players recorded acute, noncontact hamstring injuries, resulting in 3.3 ± 2.8 weeks of missed match play per injury. At preseason testing, the players who ended up sustaining a hamstring injury during the season recorded significantly higher mean hamstring stiffness (11%, P = .04) and leg stiffness (5%, P = .03). When considering the injured players, the leg stiffness of the involved limb was significantly higher than the noninjured players (P = .02), whereas hamstring stiffness was significantly higher on the noninvolved limb (P = .01). Further, those players who suffered a hamstring injury were significantly older than the noninjured players (P = .01). Conclusion: It appears that a high bilateral hamstring stiffness and leg stiffness may be a determinant in the risk of sustaining a hamstring injury. Further, relatively lower hamstring stiffness in the involved limb of injured players appears to be associated with increased injury and may be related to a lack of strength. The information from stiffness assessment may allow medical staff to determine the hamstring risk status for individual players in team sports.

Methods: Maximal isometric torque (MIT) and isometric RTD of the quadriceps were assessed in 21 trained male cyclists (28.7 ± 9.5 yr, 1.74 ± 0.08 m, and 67.5 ± 7.2 kg). Unilateral musculoarticular (MA) stiffness of the quadriceps was quantified using an oscillation test. Further, the participants performed a maximal 6-s sprint to assess peak power output (POpeak), peak CT (CTpeak), peak RCTD (RCTDpeak), and the crank angles associated with CTpeak and RCTDpeak. Participants were ranked on MA stiffness properties and were divided into a relatively stiff group (SG) and a relatively compliant group (CG). Results: The SG displayed a significantly higher MA stiffness than the CG (P < 0.05). Furthermore, the SG reported significantly elevated MIT (27%), RTD (26%), and RCTDpeak (16%) when compared with the CG (P < 0.05), along with trends for increased POpeak (7%) and CTpeak (8%). The angles at CTpeak and RCTDpeak were 7% and 12% lower for the SG, respectively (P < 0.05). MA stiffness was significantly correlated with RCTDpeak, MIT, RTD, and POpeak. Conclusions: Higher stiffness is related to superior RCTDpeak in trained cyclists during a single sprint. A significant proportion of the variance in RCTDpeak was attributed to MA stiffness (37%), which was of greater magnitude than the relationship between RCTDpeak and MIT. Furthermore, the lower CTpeak angle and RCTDpeak angle may contribute to a more rapid development of CT. Accordingly, MA stiffness seems to be an important consideration for sprint cycling....

Time-motion analysis has been extensively used to provide an insight into the movement patterns of athletes in team sports. However, few studies have indicated whether analysis involved breaks in play, or all the activities within the standard clock-time. Therefore, the purpose of this study was to differentiate whether differences existing between clock-time and match-time time-motion analysis had an affect on the results. Data was collected from the Australian National Futsal Team. Analysis was initially conducted against the actual playing clock-time of 40 min, then on a match-time of 70 min including all actions and breaks in play, excluding half-time and time-out intervals. When comparing match-time to clock-time data, there was a 16% greater total overall distance covered, a 6.9% increase in the time spent in low-intensity activities, and a change in activity every 4.00 s vs. 3.28 s, respectively. There was little change in the high-intensity activities from clock-time to match-time comparison. As clock-time data may misrepresent the movement patterns of the game, it is important to establish a standard method of analysis, namely based on match-time data. This may have implications on similar sports based on clock-time such as water polo, basketball, ice hockey and handball.

Given the popularity of running-based sports and the rapid development of Micro-electromechanical systems (MEMS), portable wireless sensors can provide in-field monitoring and analysis of running gait parameters during exercise. This paper proposed an intelligent analysis system from wireless micro–Inertial Measurement Unit (IMU) data to estimate contact time (CT) and flight time (FT) during running based on gyroscope and accelerometer sensors in a single location (ankle). Furthermore, a pre-processing system that detected the running period was introduced to analyse and enhance CT and FT detection accuracy and reduce noise. Results showed pre-processing successfully detected the designated running periods to remove noise of non-running periods. Furthermore, accelerometer and gyroscope algorithms showed good consistency within 95% confidence interval, and average absolute error of 31.53 ms and 24.77 ms, respectively. In turn, the combined system obtained a consistency of 84–100% agreement within tolerance values of 50 ms and 30 ms, respectively. Interestingly, both accuracy and consistency showed a decreasing trend as speed increased (36% at high-speed fore-foot strike). Successful CT and FT detection and output validation with consistency checking algorithms make in-field measurement of running gait possible using ankle-worn IMU sensors. Accordingly, accurate IMU-based gait analysis from gyroscope and accelerometer information can inform future research on in-field gait analysis.