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Impact of the optimal minimum velocity threshold on the accuracy of one repetition maximum estimation-based on a mixed model
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Purpose: This study aimed at developing two mixed methods approaches for 1RM prediction (generalized equations together with the individual load–velocity relationship—LVR). The validity of such equations was explored and compared with the validity of individual LVRs.
Methods: The submaximal LVRs of seventy-six males was obtained for the free-weight back squat. Theoretical load at zero velocity (LD0), the slope of the individual LVR, and the individual (ModelIND) and optimal minimum velocity thresholds (MVT) (ModelOPT) were selected to develop the equations. Prediction accuracy was determined through mean differences, absolute percent errors and Bland–Altman plots.
Results: ModelIND was predictive of 1RM (p < 0.0001), explaining 89.7% of its variance. ModelOPT increased the prediction power of 1RM to 98.7% (p < 0.0001). No significant differences in the absolute percent errors were found (4.7 and 3.9%, for ModelIND and ModelOPT, respectively). The mean difference between actual and estimated 1RM was nearly zero for both models, while individual LVRs relying on the individual MVT displayed the contrary. The limits of agreement were 15.1 and 11.4 kg for ModelIND and ModelOPT, respectively.
Conclusion: The individual and optimal MVTs add predictive value to LD0 and the slope of the individual LVR for 1RM estimation on a group level and avoid error trends over the entire muscular strength spectrum. However, only ModelIND increased the accuracy of estimations when compared with the more direct approach. From a practical standpoint, practitioners are encouraged to rely on the individual LVR instead of ModelOPT, when using the optimal MVT for estimation purposes.
Descrição
Palavras-chave
Maximum strength Resistance training Velocity-based training
Contexto Educativo
Citação
Fitas, A., Gomes, M., Santos, P. et al. Impact of the optimal minimum velocity threshold on the accuracy of one repetition maximum estimation-based on a mixed model. Sport Sci Health 21, 1801–1810 (2025). https://doi.org/10.1007/s11332-025-01407-9
Editora
Springer Nature