Music appears to have advantageous effects during exercise as it has been shown to increase motivation, decrease ratings of perceived exertion (RPE), and improve exercise performance. The improvement of running performance in particular, is the main effect recreational runners want to focus on as many want to improve their time in road races. Running velocity, indicated by a faster time to completion is thought to improve with an increase in step frequency, step length, or a combination of these variables. Currently, there is limited research that observes methods to help increase step frequency or step length. The manipulation of music cadence may be considered an effective training method that influences step frequency. The purpose of this study was to observe the effect of music cadence on step frequency in the recreational runner. A total of 30 recreational runners (15 male, 15 female) with a mean age of 31.0 ± 5.8 (range= 20-39), a BMI of 24.0 ± 3.3 (range= 19-32), and an estimated VO2 max of 49.6 ± 5.4 (range= 41-60) participated in this study. Individually, runners completed four 1600-meter time trials on an outdoor track at maximal effort. The first trial was a familiarization trial where participants ran 1600 meters with no music. Two music conditions were then created for each runner based on their natural step frequency obtained during the familiarization trial. The first condition represented music at natural step cadence and the second condition represented music at increased step cadence. The next three 1600 meter time trials were randomized and included a control condition of no music, a natural cadence music condition at the runner's natural step frequency, and an increased cadence music condition of 10-20 beats per minute (bpm) above the natural cadence music condition. Results indicated that step frequency was significantly higher during the increased cadence music condition compared to both the natural cadence music and no music conditions (p < 0.05). Additionally, time to completion was significantly lower in the increased cadence music condition compared to both the natural cadence music and no music conditions (p < 0.05). Lastly, there was no significant difference in step length among music conditions. These results indicate that music cadence had a positive influence on running performance as it was able to increase step frequency and improve time to completion in a group of recreational runners. Therefore, for practical application, recreational runners can alter music cadence to help improve running performance.

The relationships between selected sprint specific bounding exercises and sprint performance were analysed using fourteen sprint athletes (7 elite performers, 7 sub-elite performers). Subjects were required to perform sprints over 60m, Counter Movement Jumps with and without loading (20kg), High Speed Alternate Leg Bounding over 30m and High Speed Single Leg Hopping over 20m. All athletes were subject to anthropometric measurement (Height, Weight and Leg Length). Of all variables measured, the Elite group were significantly better (p<0.001) in Counter Movement Jump, Time to 60m, Time from 30m to 60m and in their Maximal Running Velocity. Linear regressions were carried out on all variables that correlated with Time to 30m (Acceleration Phase) and Maximal Running Velocity at both the pO.OOl and p<0.01 level of significance. This allowed several prediction tables to be compiled that had performance measures (sprints and jumps) that could be used as testing measures for sprint athletes to determine their Acceleration Phase and Maximal Running Velocity. A stepwise multiple regression demonstrated that Time to 60m was the best predictor of Maximal Running Velocity. Time to 60m, Leg length, High Speed Alternate Leg Bounding and Sprint Stride Rate were the best predictors of the Acceleration Phase. A Stepwise cross-validation linear discriminant function analysis was used to determine the best predictors from both sprint and jump measures that would distinguish an athlete as an elite or sub-elite performer. From sprint variables, Time to 60m and Time to 30m were the two variables that best placed a sprint subject in either the Elite or Sub-elite group. From the bounding variables, Counter Movement Jump and the Ground Contact Time of the High Speed Alternate Leg Bounding were the two variables that best placed a sprint subject in either the Elite or Sub-elite group. The present study suggests that Time to 60m is the best predictor of Maximal Running Velocity and Acceleration Phase. Counter Movement Jumping and High speed Alternate Leg Bounding are also useful tools in developing and testing elite sprint athlete performance.

The objective of this study was to determine sprint athletes and non–athletes girls acute muscles and heart adaptation peculiarities to multiple anaerobic loads. The study‘s participants were girls of similar age: 5 trained athletes in sprint events and 5 non–athletes. There were estimated their antropometric parametres, assessed body mass index (BMI) and heart rate at rest. Also participants performed multiple anaerobic workloads. The results of this research showed that girls well–trained athletes managed to perform greater extent of physical load, were less tired and managed faster to recover between reiterations. The non–athletes performed significantly lower extent of physical load and before the third workload their heart rate‘s indices and results of running 10 m distance with 10 % of body mass weighting indicated considerable fatigue. In consideration of long term adaptation effect we propose, that well–trained athletes have lower body mass, BMI, heart rate at rest, higher running velocity and greater increase of velocity compare to non–athletes. Eaqual duration, intensity, rest pauses and the number of reiterations with and without weighting have different effect on muscle working ability. Athletes showed greater running velocity and it remained unaltered during reiterations in performing eaqual workload without weighting. Participants reached lower velocity and there were obtained velocity reduction in performance with weighting.

Previous studies have used accelerometers with various operating ranges (ORs) when measuring biomechanical parameters. However, it is still unclear whether ORs influence the accuracy of running parameters, and whether the different stiffnesses of footwear midsoles influence this accuracy. The purpose of the present study was to systematically investigate the influence of OR on the accuracy of stride length, running velocity, and on peak tibial acceleration. Twenty-one recreational heel strike runners ran on a 15-m indoor track at self-selected running speeds in three footwear conditions (low to high midsole stiffness). Runners were equipped with an inertial measurement unit (IMU) affixed to the heel cup of the right shoe and with a uniaxial accelerometer at the right tibia. Accelerometers (at the tibia and included in the IMU) with a high OR of ±70 g were used as the reference and the data were cut at ±32, ±16, and at ±8 g in post-processing, before calculating parameters. The results show that the OR influenced the outcomes of all investigated parameters, which were not influenced by tested footwear conditions. The lower ORs were associated with an underestimation error for all biomechanical parameters, which increased noticeably with a decreasing OR. It can be concluded that accelerometers with a minimum OR of ±32 g should be used to avoid inaccurate measurements.

Previous studies have used accelerometers with various operating ranges (ORs) when measuring biomechanical parameters. However, it is still unclear whether ORs influence the accuracy of running parameters, and whether the different stiffnesses of footwear midsoles influence this accuracy. The purpose of the present study was to systematically investigate the influence of OR on the accuracy of stride length, running velocity, and on peak tibial acceleration. Twenty-one recreational heel strike runners ran on a 15-m indoor track at self-selected running speeds in three footwear conditions (low to high midsole stiffness). Runners were equipped with an inertial measurement unit (IMU) affixed to the heel cup of the right shoe and with a uniaxial accelerometer at the right tibia. Accelerometers (at the tibia and included in the IMU) with a high OR of ±70 g were used as the reference and the data were cut at ±32, ±16, and at ±8 g in post-processing, before calculating parameters. The results show that the OR influenced the outcomes of all investigated parameters, which were not influenced by tested footwear conditions. The lower ORs were associated with an underestimation error for all biomechanical parameters, which increased noticeably with a decreasing OR. It can be concluded that accelerometers with a minimum OR of ±32 g should be used to avoid inaccurate measurements.