Alejandro Muñoz-López1, Marco Pozzo2, Pablo Floria3. 1. Departamento de Motricidad Humana y Rendimiento Deportivo, University of Seville, Seville, Spain. Electronic address: amunlop@gmail.com. 2. SmartCoach Technologies, CA, USA. 3. Physical Performance & Sports Research, Universidad Pablo de Olavide, Seville, Spain.
Abstract
PURPOSE: This study aimed to analyze mechanical performance and mechanical losses across different sets and repetitions of unilateral leg extensions under flywheel loading conditions. METHODS: In a cross-over design, 23 physically active participants executed 3 sets of 30 repetitions using two different loads. Angular speed, angular acceleration and power were monitored, and mean and peak values of these variables were calculated for each set of repetitions. We analyzed time-course differences every five repetitions (described as 'clusters'). Differences in the mechanical profile for Load by Set and in the mechanical losses for Load by Set by Cluster repetitions were analyzed using an ANOVA repeated measures test. A Principal Components (PC) analysis was also conducted. RESULTS: Only peak acceleration showed a significant Set by Load interaction (p = 0.006) in mechanical performance. At the intra-set level, all consecutive clusters showed a significant decrement in all variables, but with different magnitudes (speed < acceleration < power). Although all variables showed significant Set by Cluster and Load by Cluster interactions, only the acceleration variables showed a significant Set by Load interaction (p < 0.05). All the variables showed a single PC. Higher fatigue was found around the concentric peak for each variable. CONCLUSIONS: The most sensitive variable for detecting fatigue using real-time feedback seems to be the angular acceleration of the flywheel. Peak acceleration can be used to monitor training workload and training volume in real time in leg extension exercises using flywheel training devices.
PURPOSE: This study aimed to analyze mechanical performance and mechanical losses across different sets and repetitions of unilateral leg extensions under flywheel loading conditions. METHODS: In a cross-over design, 23 physically active participants executed 3 sets of 30 repetitions using two different loads. Angular speed, angular acceleration and power were monitored, and mean and peak values of these variables were calculated for each set of repetitions. We analyzed time-course differences every five repetitions (described as 'clusters'). Differences in the mechanical profile for Load by Set and in the mechanical losses for Load by Set by Cluster repetitions were analyzed using an ANOVA repeated measures test. A Principal Components (PC) analysis was also conducted. RESULTS: Only peak acceleration showed a significant Set by Load interaction (p = 0.006) in mechanical performance. At the intra-set level, all consecutive clusters showed a significant decrement in all variables, but with different magnitudes (speed < acceleration < power). Although all variables showed significant Set by Cluster and Load by Cluster interactions, only the acceleration variables showed a significant Set by Load interaction (p < 0.05). All the variables showed a single PC. Higher fatigue was found around the concentric peak for each variable. CONCLUSIONS: The most sensitive variable for detecting fatigue using real-time feedback seems to be the angular acceleration of the flywheel. Peak acceleration can be used to monitor training workload and training volume in real time in leg extension exercises using flywheel training devices.