Biology of Sport

Abstract

1/2026 vol. 43
Original paper

Force vs. impulse: assessing the applicability of dynamic strength indices in individualized training recommendations

  1. School of Education, Beijing Sport University, Beijing, China
  2. London Sports Institute, Middlesex University, London, United Kingdom
  3. Sport, Performance, and Nutrition Research Group, School of Allied Health, Human Services, & Sport, La Trobe University, Melbourne, VIC, Australia
  4. School of Sport Science, Beijing Sport University, Beijing, China
  5. School of Strength and Conditioning, Beijing Sport University, Beijing, China
  6. The Key Laboratory of Sports Training, General Administration of Sport of China, Beijing, China
Biol Sport. 2026;43:743–752
Online publish date: 2026/01/02
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In this cross-sectional investigation, we aimed to compare impulse-derived (iDSI) and forcederived (fDSI) dynamic strength indices to determine their consistency in guiding individualized training recommendations. It also assessed the agreement between training prescriptions based on these indices and analyzed individualized neuromuscular profiles through case study comparisons. Twenty male skeleton and bobsled athletes performed countermovement jump (CMJ) and isometric mid-thigh pull (IMTP) assessments in a counterbalanced order. Wilcoxon signed-rank tests were conducted to examine differences between fDSI and iDSI. Spearman correlation coefficients quantified intra-group relationships, and linear regression analysis evaluated the model fit between indices. Cohen’s kappa (κ) was applied to assess agreement in training classifications derived from fDSI and iDSI. Both indices showed limitations in representing lower-limb neuromuscular function (Z = –3.72, p < 0.01, large effect). A moderate correlation was observed between iDSI and fDSI (rs = 0.47, p < 0.05), with moderate agreement in their training recommendations (κ = 0.52, 95% CI: [0.38, 0.66]). Case-study analyses revealed substantial inter-athlete variability in CMJ force–time characteristics, highlighting the need for individualized interpretation within performance profiling. Incorporating phase-specific, multi-metric evaluations of force–time variables may improve the precision of training decisions and better inform athletespecific programming.
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