Abstract

Background: The ability for an athlete to express force maximally and rapidly is key for athletic success. A popular method for obtaining key metrics involves holding a testing day or days, that ultimately are time consuming and fatiguing. These testing days usually provide one metric per assessment and do not demonstrate any interactions between assessments. This could poorly guide training programs as it might not provide the full picture of an athlete. A quick and accessible assessment method that can obtain key athlete data frequently is crucial for practitioners. Furthermore, the ratio between rate of force development and maximum strength obtained from said assessments could better dictate training plans.
Purpose: To identify the relationship between isometric Rate of Force Development (_RFD) and isometric maximum strength ratio to dynamic performance assessments and determine if thresholds exist that support superior performance.
Methods: Data was obtained from nineteen subjects in the youth athletic population (n=19 (male n=14, female n=5)), age: 14±2.49, height:165.46±12.75cm, mass: 55.39±19.77kg (lean body mass: 47.20±15.99kg, percent body 15.11±0.085%) and included various backgrounds and sports including lacrosse, basketball, football, swimming, motorcycle racing, track, soccer and baseball. Data was procured retrospectively. Assessments included body composition (total body mass, fat mass, fat-free mass, and height), isometric mid-thigh pull RFD (IMTP_rfd), isometric mid-thigh pull max strength (IMTP_max), counter movement jump height (CMJ), drop jump height and ground contact time (DRJ_h and DRJ_gct), maximum sprint time at 10 and 20 meters (Sprint_10m and Sprint _20m) and change of direction (COD5-10-5). Statistics were run for all participants and then broken into three groups consisting of high, medium, and low threshold groups. A Pearson correlation coefficient was run to determine strength and direction of linear association between variables and p-value to determine level of significance.
Results: The low ratio threshold group had a moderate negative relationship with COD5-10-5 (r=-0.62). This was found too not be statistically significant. The low ratio threshold group also had a low negative correlation (r=-0.34) with DRJgct. This was also found too not be statistically significant. The other variables (CMJ, DRJ_h, Sprint_10m and Sprint_20m) when compared to the low threshold group yielded negligible correlations (r<0.30) that were not statistically significant. The medium threshold group compared to traditional field tests yielded negligible correlation (r<0.30) across all variables (CMJ, DRJ_h, DRJgct, Sprint_10m, Sprint_20m, COD5-10-5). Additionally, no variables were statistically significant. The high threshold group of IMTP_rfd:IMTP_max had a very high positive correlation with CMJ (r=0.91) while unfortunately having no statistical significance. Additionally, there was a moderate positive correlation with IMTP_rfd:IMTP_max and DRJ_h (r=0.63), again no statistical significance was found. There were high to very high negative correlations found between IMTP_rfd:IMTP_max and Sprint_10m (r=-0.72), Sprint_20m(r=-0.79) and COD5-10-5 (r=-0.92). There was statistical significance for any other variables. When DRJgct was compared to the high threshold group there was a negligible correlation (r<0.30) that was not statistically significant.
Conclusion: Our findings suggests that the ratio between IMTP_rfd:IMTP_max does correlate to dynamic performance and furthermore there are thresholds that exists that could be utilized to better dictate an athlete’s training program.
Practical application: Practitioners working with youth athletes should take into consideration using an assessment method such as the IMTP to evaluate their athlete’s performance and progress. Additionally, practitioners should consider the ratio between IMTP_rfd:IMTP_max of their athletes to identify whether an athlete requires a specific training stimulus to enhance performance capabilities.