In the era of artificial intelligence, the visualization and quantification of athletes' physical fitness have become critical areas of focus in the fields of physical and sports training. The Dynamic Strength Index (DSI) is a key metric used to assess neuromuscular function. However, the traditional force-based DSI (fDSI) overlooks time-dependent force characteristics, which may limit its applicability in training prescriptions. This study aimed to compare two dynamic strength indices—the force-based dynamic strength index (fDSI) and the impulse-based dynamic strength index (iDSI)—in relation to force-time performance variables and to assess their applicability in personalized training recommendations. Case study analyses were conducted to provide individualized training suggestions based on athlete-specific profiles, ensuring that interventions address the unique needs and characteristics of each athlete. Method: Twenty male skeleton and bobsled athletes performed countermovement jump (CMJ) and isometric mid-thigh pull (IMTP) tests in a counterbalanced order. Impulse and peak force values were calculated using integration equations. Wilcoxon signed-rank tests were employed to compare the evaluation differences between fDSI and iDSI, and effect sizes were reported using Cliff's delta. Spearman correlation coefficients were used to examine intra-group relationships, and linear regression models were applied to evaluate the fit between fDSI and iDSI. Kappa analysis was conducted to assess discrepancies in training recommendations derived from these indices. The results indicated that both indices exhibited limitations in assessing lower-limb neuromuscular function (Z = -3.72, p 0.05). Linear regression analysis demonstrated poor model fit (R² = 0.0593, p = 0.32), and the agreement between the two indices was moderate (Kappa = 0.52, 95% CI: 0.38, 0.66). Case study analyses revealed considerable inter-athlete variability in percentile rankings for CMJ force-time characteristics. This study confirms that both force-based (fDSI) and impulse-based (iDSI) dynamic strength indices have inherent dimensional limitations in assessing lower-limb neuromuscular function. Although these indices exhibit moderate agreement in training recommendations, their divergence rate reaches 45%. Practitioners should incorporate additional data, such as percentile rankings, time-normalized force-time characteristics, and other performance metrics, when designing training interventions.
Liu et al. (Thu,) studied this question.