Novel Approaches to Measurement of Passive Torque and Stiffness in the Hamstrings

Abstract

Recent evidence related to passive force enhancement (PFE, Herzog & Leonard, 2002) and the calculation of passive stiffness (Nordez et al., 2006) show that the measurement of passive torque may need to be revisited. The purpose of this study was to compare a novel assessment of passive resistance to the traditional assessment. Mathematical models were used to determine model goodness of fit between experimental and traditional conditions and these models were used to predict maximal passive stiffness (MPS). Peak passive resistance was significantly lower (p < .001) after a 1-minute 3-minute, and 5-minute rest period and resetting to resting length in comparison to the traditional measurement. Among the experimental conditions, 1-minute was significantly lower than 3-minutes of rest and resetting to resting length (p = .039), while the other comparisons were not significantly different. These results show that passive resistance is lower in the novel assessment that considers PFE compared to the traditional methodology. In the second-order polynomial model, root mean squared error (RMSE) was significantly lower (p = .011) after a 1-minute rest period, a 3-minute rest period (p = 0.23), and a 5-minute rest period and resetting to resting length (p = .028), compared to the traditional measurement. In the fourth-order polynomial model, RMSE was significantly lower (p < .001) after a 1-minute, a 3-minute (p = .002), and a 5-minute rest period and resetting to resting length (p = .004), compared to the traditional measurement. In the exponential model, RMSE was significantly lower (p < .001) after a 1-minute, a 3-minute (p < .001), and a 5-minute rest period and resetting to resting length (p < .001), compared to the traditional measurement. Comparisons among the experimental conditions in each model were not significantly different. When observed MPS was compared to predicted MPS, all comparisons among each testing condition and each mathematical model were significantly different (p < .001). These results show that PFE is likely to not be involved in the experimental conditions as they show a better fit to each mathematical model. Further, no one model appeared to predict MPS better than the others.