A Novel Approach to 1RM Prediction Using the Load-Velocity Profile: A Comparison of Models

Thompson, S.W.N.; Rogerson, David; Ruddock, Alan; Greig, Leon; Dorrell, Harry F.; Barnes, Andrew

doi:10.3390/sports9070088

Cited by 23 publications

(32 citation statements)

References 41 publications

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“…The absence of a maximal effort cue in the present study, by design, provides a meaningful comparison with another recent study that did utilize the maximal effort cue with well-trained males in the back squat. Thompson and colleagues (2021) found that a mixed exercise (jump squats and back squats) quadratic prediction equation was able to predict 1RM value within ±3kg , further supporting that providing a maximal intended velocity cue may be necessary for submaximal velocity to accurately predict 1RM value in various exercises (Thompson et al, 2021).…”

Section: Discussionmentioning

confidence: 95%

Accuracy of Predicting One-Repetition Maximum from Submaximal Velocity in the Barbell Back Squat and Bench Press

Macarilla

Sautter

Robinson

et al. 2022

Journal of Human Kinetics

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This study examined the accuracy of predicting a free-weight back squat and a bench press one-repetition maximum (1RM) using both 2- and 4-point submaximal average concentric velocity (ACV) methods. Seventeen resistance trained men performed a warm-up and a 1RM test on the squat and bench press with ACV assessed on all repetitions. The ACVs during the warm-up closest to 1.0 and 0.5m.s-1 were used in the 2-point linear regression forecast of the 1RM and the ACVs established at loads closest to 20, 50, 70, and 80% of the 1RM were used in the 4-point 1RM prediction. Repeated measures ANOVA and Bland-Altman and Mountain plots were used to analyze agreement between predicted and actual 1RMs. ANOVA indicated significant differences between the predicted and the actual 1RM for both the 2- and 4-point equations in both exercises (p<0.001). The 2-point squat prediction overestimated the 1RM by 29.12±0.07kg and the 4-point squat prediction overestimated the 1RM by 38.53±5.01kg. The bench press 1RM was overestimated by 9.32±4.68kg with the 2-point method and by 7.15±6.66kg using the 4-point method. Bland-Altman and Mountain plots confirmed the ANOVA findings as data were not tightly conformed to the respective zero difference lines and Bland-Altman plots showed wide limits of agreement. These data demonstrate that both 2- and 4-point velocity methods predicted the bench press 1RM more accurately than the squat 1RM. However, a lack of agreement between the predicted and the actual 1RM was observed for both exercises when volitional velocity was used.

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Section: Discussionmentioning

confidence: 95%

Accuracy of Predicting One-Repetition Maximum from Submaximal Velocity in the Barbell Back Squat and Bench Press

Macarilla

Sautter

Robinson

et al. 2022

Journal of Human Kinetics

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“…The grouped effect sizes for magnitude of difference between predicted and actual 1RM are presented graphically in Fig 2 . Twenty studies (80%) included male participants only [ 6 , 9 , 15 , 17 – 19 , 28 – 41 ], three studies (12%) feature both male and female participants [ 12 , 13 , 42 ], one study (4%) included only females participants [ 43 ] and one study (4%) did not describe the sex of participants [ 44 ]. The training experience of participants also varied substantially across each of the studies included.…”

Section: Resultsmentioning

confidence: 99%

“…Caven and associates reported larger effect sizes using 2-load predictions of bench press (ES = 0.32 to 0.33) and squat (ES = 0.23 to 0.29) 1RM when compared to a model using 8-loads (bench press ES = 0.10 to 0.11; squat ES = 0.16 to 0.19) [ 43 ]. The remaining studies highlighted no differences between predictions performed using 2- vs. 4-load [ 30 , 31 , 40 , 42 ], 2- vs. 5-load [ 18 , 19 ], 2- vs. 7-load [ 39 ], 4- vs. 7-load [ 41 ], 4- vs. 7-load [ 36 ], 2- vs. 3- vs. 4-load [ 12 ], or 3- vs.4- vs. 5-load models [ 34 ]. Despite minimal evidence that validity differs between models, the mean grouped effect size appears to increase (i.e., poorer validity) when a greater number of loads are used.…”

Section: Discussionmentioning

confidence: 99%

“…Linear versus polynomial regressions. Several studies have administered both linear and polynomial regression models for the identical number of loads [ 18 , 40 , 41 ] with little consensus on the ideal model to use ( Table 4 ). For the deadlift, Jukic et al[ 18 ] suggest that linear regression models may provide more valid 1RM estimates when compared to polynomial models.…”

Section: Discussionmentioning

confidence: 99%

“…Significantly greater bench press validity has been reported when using 4-load linear regressions (SEE = 2.79 ± 2.29 kg) when compared to polynomial regression models (SEE = 3.54 ± 3.31 kg); albeit the relative standard error (linear = 4.6 to 5.4%; polynomial ≈ 5.3 to 5.7%) was marginal between models overall [ 40 ]. In contrast, 4- and 7-load linear regression models were determined elsewhere to underestimate back squat 1RM when compared to quadratic polynomial models [ 41 ]. This may be due to the exercise selection; the Smith machine was used previously by Janicijevic et al[ 40 ], whereas Thompson et al [ 41 ] examined the back squat using free-weights.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Load-velocity relationships and predicted maximal strength: A systematic review of the validity and reliability of current methods

Marston

Forrest²,

Teo³

et al. 2022

PLoS ONE

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Maximal strength can be predicted from the load-velocity relationship (LVR), although it is important to understand methodological approaches which ensure the validity and reliability of these strength predictions. The aim of this systematic review was to determine factors which influence the validity of maximal strength predictions from the LVR, and secondarily to highlight the effects of these factors on the reliability of predictions. A search strategy was developed and implemented in PubMed, Scopus, Web of Science and CINAHL databases. Rayyan software was used to screen titles, abstracts, and full texts to determine their inclusion/eligibility. Eligible studies compared direct assessments of one-repetition maximum (1RM) with predictions performed using the LVR and reported prediction validity. Validity was extracted and represented graphically via effect size forest plots. Twenty-five eligible studies were included and comprised of a total of 842 participants, three different 1RM prediction methods, 16 different exercises, and 12 different velocity monitoring devices. Four primary factors appear relevant to the efficacy of predicting 1RM: the number of loads used, the exercise examined, the velocity metric used, and the velocity monitoring device. Additionally, the specific loads, provision of velocity feedback, use of lifting straps and regression model used may require further consideration.

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Analyzing one-repetition-max predictions: load-velocity relationship vs. repetition to failure equation in ten lower extremity exercises

Simonsen,

Jolas,

Pedersen

et al. 2024

Sport Sci Health

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Purpose This study aims to evaluate the accuracy of predicting one-repetition-maximum (1RM) using the load-velocity relationship and different repetition-to-failure estimation equations for ten lower-extremity exercises. Methods A total of 22 healthy participants were recruited. The tested exercises included ankle, knee, and hip joint flexion and extension, as well as hip abduction, hip adduction, and leg press. Velocity during the concentric phase was measured using a linear transducer, and individual linear regression models were established using incremental submaximal loads (40–80% 1RM) and velocity to estimate the 1RM. Repetition-to-failure estimations of 1 RM were assessed with eleven different regression equations, among them the Lombardi equation. Intraclass correlation coefficient (ICC), Bland and Altman plots, and normalized mean absolute error (NMAE) were used to compare the estimations with a measured 1RM. Results Predictions based on the load-velocity relationship exhibited NMAE values ranging from 8.6% to 35.2%, ICC values from 0.35 to 0.87, and substantial limits of agreement across all exercises, in contrast to the measured 1RM values. Among the fatigue estimation equations, the Lombardi equation demonstrated the lowest NMAE across all exercises (5.8%), with an excellent ICC of 0.99 and narrow limits of agreement. Conclusion The load-velocity relationship proved inadequate for predicting 1RM in lower-extremity single-joint exercises. However, the Lombardi estimation equations showcased favorable predictive performance with a consistently low average NMAE across all exercises studied.

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A Novel Approach to 1RM Prediction Using the Load-Velocity Profile: A Comparison of Models

Cited by 23 publications

References 41 publications

Accuracy of Predicting One-Repetition Maximum from Submaximal Velocity in the Barbell Back Squat and Bench Press

Accuracy of Predicting One-Repetition Maximum from Submaximal Velocity in the Barbell Back Squat and Bench Press

Load-velocity relationships and predicted maximal strength: A systematic review of the validity and reliability of current methods

Analyzing one-repetition-max predictions: load-velocity relationship vs. repetition to failure equation in ten lower extremity exercises

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