Reliability and validity of the multi-point method and the 2-point method's variations of estimating the one-repetition maximum for deadlift and back squat exercises

Optimal Minimum-Velocity Threshold to Predict One-repetition Maximum for the Back Squat

The prediction of one-repetition maximum (1RM) is highly relevant for strength and conditioning. The optimal minimum-velocity threshold (MVT) was recently proposed to increase the accuracy of 1RM predictions. Individual load-velocity profiles (LVP) were obtained in 18 athletes enrolled in recreational soccer. Reliability analyses were computed for all LVP-derived variables. Estimations of 1RM were made based on general (0.3 m.s- 1), pre-individual (mean velocity at 1RM obtained in a preliminary session) and optimal MVT (velocity that eliminates the difference between actual and predicted 1RM, determined in a preliminary session). The accuracy of 1RM predictions was examined using absolute-percent error and Bland-Altman plots. Between-day reliability of the LVP and 1RM was good (intraclass-correlation coefficients - ICCs>0.9 and coefficients of variation - CVs<5%). The individual and optimal MVT reached moderate-to-good reliability (ICCs>0.9 and CVs<10%, respectively). The predictions based on the optimal MVT displayed greater accuracy than those obtained with the individual and general MVT (absolute percent error: 2.8 vs. 5.5 vs. 4.9%, respectively). However, wide limits of agreement (LoA) were found between actual and estimated 1RM using this approach (~15 kg). Data indicate that the optimal MVT provides better estimations of 1RM for the free-weight back squat than the general and the individual MVT.

Average optimal minimum velocity threshold: A practical variable to increase the accuracy of one-repetition maximum estimation during the free-weight back squat

The prediction of one-repetition maximum (1RM) using the submaximal load-velocity relationship (LVR) is highly relevant for the field of strength and conditioning. The optimal minimum velocity threshold (MVT) was recently proposed to increase the accuracy of 1RM predictions. However, using the average optimal MVT would allow for more practical estimations. LVRs of the free-weight back squat were obtained in 53 participants, throughout 2 sessions. LVRs were obtained using the multi- and two-point methods. Estimations of 1RM were made based on the average actual MVT (1RM velocity) and the average optimal MVT. The accuracy of 1RM predictions was examined using absolute-percent error and Bland-Altman plots. Cross-validation was performed using a leave-one-out approach. The number of selected loads did not affect the slope, y-intercept, optimal MVT or the accuracy of 1RM predictions. Predictions based on the average optimal MVT displayed greater accuracy than those obtained with the average actual MVT (~6% vs. ~8% absolute-percent error, respectively). However, wide 95% limits of agreement (LoA) were found between actual and estimated 1RM using both approaches (~13%1RM). The average optimal MVT offers more accurate 1RM estimations than the average actual MVT. However, errors prove substantial, making it challenging to precisely track minor changes in 1RM.

Validation of a Single-Session Protocol to Determine the Load-Velocity Profile and One-Repetition Maximum for the Back Squat Exercise

ABSTRACT

Gomes, M, Fitas, A, Santos, P, Pezarat-Correia, P, and Mendonca, GV. Validation of a single session protocol to determine the load-velocity profile and one-repetition maximum for the back squat exercise. J Strength Cond Res 38(6): 1013-1018, 2024-We investigated whether a single session of absolute incremental loading is valid to obtain the individual load-velocity profile (LVP) and 1 repetition maximum (1RM) for the free-weight parallel back squat. Twenty strength-trained male subjects completed 3 testing sessions, including a baseline 1RM session and 2 LVP sessions (LVP rel based on incremental relative loads and LVP abs based on absolute load increments until 1RM). The 1RM load was compared between the baseline and LVP abs . The load at zero velocity (load-axis intercept [L 0 ]), maximal velocity capacity (velocity-axis intercept [V 0 ]), slope, and area under the load-velocity relationship line (A line ) were compared between the LVP rel and LVP abs using equivalence testing through 2 one-sided t -tests. Measurement accuracy was calculated using the absolute percent error. The 1RM measured at baseline and LVP abs was equivalent and presented a low absolute percent error (1.2%). The following LVP parameters were equivalent between LVP rel and LVP abs : 1RM, L 0 , and A line because the mean difference between sessions was close to zero and the Bland-Altman limits of agreement (1RM:5.3 kg; L 0 :6.8 kg; A line : 9.5 kg·m -1 ·s -1 ) were contained within the a priori defined ± equivalent margins (5% for 1RM and L 0 and 10% for A line ). The aforementioned variables presented a low absolute percent error. However, slope and V 0 were not equivalent between sessions. In conclusion, a single session of absolute incremental loading is a valid approach to obtain the L 0 and A line of the individual LVP and 1RM, and can be used to efficiently track the magnitude of neuromuscular adaptations throughout the training cycles for the free-weight back squat.

Resistance Training Intensity Prescription Methods Based on Lifting Velocity Monitoring

Resistance training intensity is commonly quantified as the load lifted relative to an individual's maximal dynamic strength. This approach, known as percent-based training, necessitates evaluating the one-repetition maximum (1RM) for the core exercises incorporated in a resistance training program. However, a major limitation of rigid percent-based training lies in the demanding nature of directly testing the 1RM from technical, physical, and psychological perspectives. A potential solution that has gained popularity in the last two decades to facilitate the implementation of percent-based training involves the estimation of the 1RM by recording the lifting velocity against submaximal loads. This review examines the three main methods for prescribing relative loads (%1RM) based on lifting velocity monitoring: (i) velocity zones, (ii) generalized load-velocity relationships, and (iii) individualized load-velocity relationships. The article concludes by discussing a number of factors that should be considered for simplifying the testing procedures while maintaining the accuracy of individualized L-V relationships to predict the 1RM and establish the resultant individualized %1RM-velocity relationship: (i) exercise selection, (ii) type of velocity variable, (iii) regression model, (iv) number of loads, (v) location of experimental points on the load-velocity relationship, (vi) minimal velocity threshold, (vii) provision of velocity feedback, and (viii) velocity monitoring device.

Prediction of One Repetition Maximum in Free-Weight Back Squat Using a Mixed Approach: The Combination of the Individual Load-Velocity Profile and Generalized Equations

ABSTRACT

Fitas, A, Santos, P, Gomes, M, Pezarat-Correia, P, Schoenfeld, BJ, and Mendonca, GV. Prediction of one repetition maximum in free-weight back squat using a mixed approach: the combination of the individual load-velocity profile and generalized equations. J Strength Cond Res 38(2): 228-235, 2024-We aimed to develop a mixed methods approach for 1 repetition maximum (1RM) prediction based on the development of generalized equations and the individual load-velocity profile (LVP), and to explore the validity of such equations for 1RM prediction. Fifty-seven young men volunteered to participate. The submaximal load-velocity relationship was obtained for the free-weight parallel back squat. The estimated load at 0 velocity (LD0) was used as a single predictor, and in combination with the slope of the individual LVP, to develop equations predictive of 1RM. Prediction accuracy was determined through the mean absolute percent error and Bland-Altman plots. LD0 was predictive of 1RM ( p < 0.0001), explaining 70.2% of its variance. Adding the slope of the LVP to the model increased the prediction power of 1RM to 84.4% ( p < 0.0001). The absolute percent error between actual and predicted 1RM was lower for the predictions combining LD0 and slope (6.9 vs. 9.6%). The mean difference between actual and estimated 1RM was nearly zero and showed heteroscedasticity for the LD0 model, but not for the combined model. The limits of agreement error were of 31.9 and 23.5 kg for LD0 and LD0 combined with slope, respectively. In conclusion, the slope of the individual LVP adds predictive value to LD0 in 1RM estimation on a group level and avoids error trends in the estimation of 1RM over the entire spectrum of muscle strength. However, the use of mixed methods does not reach acceptable accuracy for 1RM prediction of the free-weight back squat on an individual basis.

Is two-point method a valid and reliable method to predict 1RM? A systematic review

This systematic review aimed to evaluate the reliability and validity of the two-point method in predicting 1RM compared to the direct method, as well as analyze the factors influencing its accuracy. A comprehensive search of PubMed, Web of Science, Scopus, and SPORTDiscus databases was conducted. Out of the 88 initially identified studies, 16 were selected for full review, and their outcome measures were analyzed. The findings of this review indicated that the two-point method slightly overestimated 1RM (effect size = 0.203 [95%CI: 0.132, 0.275]; P < 0.001); It showed that test-retest reliability was excellent as long as the test loads were chosen reasonably (Large difference between two test loads). However, the reliability of the two-point method needs to be further verified because only three studies have tested its reliability. Factors such as exercise selection, velocity measurement device, and selection of test loads were found to influence the accuracy of predicting 1RM using the two-point method. Additionally, the choice of velocity variable, 1RM determination method, velocity feedback, and state of fatigue were identified as potential influence factors. These results provide valuable insights for practitioners in resistance training and offer directions for future research on the two-point method.

The 2-Point Method: Theoretical Basis, Methodological Considerations, Experimental Support, and Its Application Under Field Conditions

The "2-point method," originally referred to as the "2-load method," was proposed in 2016 by Prof Slobodan Jaric to characterize the maximal mechanical capacities of the muscles to produce force, velocity, and power. Two years later, in 2018, Prof Jaric and I summarized in a review article the scientific evidence showing that the 2-point method, compared with the multiple-point method, is capable of providing the outcomes of the force-velocity (F-V) and load-velocity (L-V) relationships with similar reliability and high concurrent validity. However, a major gap of our review was that, until 2018, the feasibility of the 2-point method had only been explored through testing procedures based on multiple (more than 2) loads. This is problematic because (1) it has misled users into thinking that implementing the 2-point method inevitably requires testing more than 2 conditions and (2) obtaining the data from the same test could have artificially inflated the concurrent validity of the 2-point method. To overcome these limitations, subsequent studies have implemented in separate sessions the 2-point method under field conditions (only 2 different loads applied in the testing protocol) and the standard multiple-point method. These studies consistently demonstrate that while the outcomes of the 2-point method exhibit comparable reliability, they tend to have slightly higher magnitudes compared with the standard multiple-point method. This review article emphasizes the practical aspects that should be considered when applying the 2-point method under field conditions to obtain the main outcomes of the F-V and L-V relationships.

Applicability of the Load-Velocity Relationship to Predict 1-Repetition Maximum in the Half-Squat in High-Level Sprinters

PURPOSE

To investigate the indirect measurement of 1-repetition-maximum (1RM) free-weight half-squat in high-level sprinters using the load-velocity relationship.

METHODS

Half-squat load and velocity data from 11 elite sprinters were collected in 2 separate testing sessions. Approximately 24 hours prior to the first testing session, sprinters completed a fatiguing high-intensity training session consisting of running intervals, staircase exercises, and body-weight exercises. Prior to the second testing session, sprinters had rested at least 48 hours. Two different prediction models (multiple-point method, 2-point method) were used to estimate 1RM based on the load and either mean or peak concentric velocity data of submaximal lifts (40%-90% 1RM). The criterion validity of all methods was examined through intraclass correlation coefficients, coefficient of variation (CV%), Bland-Altman plots, and the SEM.

RESULTS

None of the estimations were significantly different from the actual 1RM. The multiple-point method showed higher intraclass correlation coefficients (.91 to .97), with CVs from 3.6% to 11.7% and SEMs from 5.4% to 10.6%. The 2-point method showed slightly lower intraclass correlation coefficients (.76 to .95), with CVs 1.4% to 17.5% and SEMs from 9.8% to 26.1%. Bland-Altman plots revealed a mean random bias in estimation of 1RM for both methods (mean and peak velocity) ranging from 1.06 to 13.79 kg.

CONCLUSION

Velocity-based methods can be used to roughly estimate 1RM in elite sprinters in the rested and fatigued conditions. However, all methods showed variations that limit their applicability for accurate load prescription for individual athletes.

Test-Retest Reliability of the Functional Electromechanical Dynamometer for Squat Exercise

BACKGROUND

the main objective of this study was to evaluate the test-retest reliability of two different functional electromechanical dynamometry (FEMD)-controlled squat training protocols.

METHODS

twenty-eight healthy young adults volunteered to participate in this study. They attended the laboratory on four different days and performed four sessions: two of three sets of 12 repetitions at 75% 1RM and two of three sets of 30 repetitions at 50% 1RM. The range of movement (ROM), mean dynamic strength (MDS), peak dynamic strength (PDS), mean velocity (MV), peak velocity (PV), mean potency (MP), peak potency (PP), work (W), and impulse (I) were recorded. To evaluate the reliability of FEMD, the intraclass correlation coefficient (ICC), standard error of measurement (SEM), and coefficient of variation (CV) were obtained.

RESULTS

reliability was very high for ROM (CV: 3.72%, ICC: 0.95), MDS (CV: 1.09%, ICC: 1.00), PDS (CV: 1.97%, ICC: 1.00), and W (CV: 4.69%, ICC: 1.00) conditions at 50% 1RM and for ROM (CV: 3.90%, ICC: 0.95), MDS (CV: 0.52, ICC: 1.00), PDS (CV: 1.49%, ICC: 0.98), and W (CV: 4.14%, ICC: 1.00) conditions at 75% 1RM and high for the rest of variables at 50 and 75% 1RM.

CONCLUSIONS

this study demonstrates that FEMD is a reliable instrument to measure ROM, MDS, PDS, MV, PV, PV, MP, MP, W, and I during both squat protocols (50 and 75% 1RM) in healthy young adults.