Resistance training loads are often prescribed based on a percentage of 1 repetition maximum (1RM). Although percentage-based training is objective, fixed percentages do not take into account daily fatigue and energy levels and the 1RM may change more rapidly (1) than it can be tested in some instances. An alternative to percentage-based training is velocity-based training (VBT) in which average concentric velocity (ACV), based on barbell velocity, is used to prescribe load (9). It has been suggested that VBT could identify inappropriate training loads when other stressors on the body impede training performance (17) and, therefore, be useful for the assessment of daily training readiness. The ACV has been shown to change with fatigue during acute performance (13); however, the utility of VBT has also been questioned in its use as a reliable method for monitoring training load (2). A daily 1RM can be predicted based on ACV measures from submaximal loads (12,14,16,21), although this method may overestimate the actual 1RM (2). Technologies such as linear position transducers have become more accessible to practitioners for barbell velocity measurement (11), and these devices typically can calculate peak concentric velocity (PCV) in addition to ACV. Peak concentric velocity could be related to high-velocity activities such as the 36.6-m sprint because it excludes the “sticking region” of the repetition (during which velocity would be the lowest); PCV may be related to an athlete's ability to exert maximal force at higher velocities. Currently, there are no data investigating the relationship between PCV and sprinting speed.
Although VBT can be an attractive method for coaches and trainees, current recommendations for optimizing training adaptations using VBT can be broad. For example, it has been recommended that when training for absolute strength, ACV of a lift should be between 0.15 and 0.30 m·s−1 depending on the range of motion (17). Recent studies have examined ACV in the back squat (2–4,12,13,16,21,22), bench press (6,7,12,14,21), and deadlift (12). Although there is a clear inverse relationship between relative load and ACV, there is substantial between-subject variation in ACV at a given load. Several potential sources of this between-subject variability have been suggested including anthropometric factors (i.e., limb length) (12), use of the stretch-shortening cycle between the eccentric and concentric portion of lift (6), experience or training age (4), as well as the relative load (%1RM) because higher relative loads may increase between-subject variability (3). As training age increases, ACV may decrease at 1RM as the trainee becomes stronger. Supporting this notion, novice squatters had a higher ACV (0.34 ± 0.07 m·s−1) compared with experienced squatters (0.24 ± 0.04 m·s−1) during a 1RM back squat (22). In addition, longer femur length could be related to greater ACV in squatting exercises because of increased range of motion throughout a repetition. Furthermore, much of the previous velocity data have been collected on a resistance-trained population, but not specific sport athletes. Thus, data are needed to not only further elucidate which factors may influence ACV to individualize VBT but also to investigate these factors in team sport athletes. Secondarily, it may be useful to coaches to identify the relationships between resistance exercise velocity measures and other training outcomes such as sprinting speed.
Therefore, the primary purpose of this study was to examine relationships between training age, femur length, and ACV during a 1RM back squat in collegiate athletes. We hypothesized that ACV would be inversely related to training age, in that high training age would lead to lower ACV at 1RM. We also hypothesized that there would be a positive relationship between femur length and ACV, in that longer femurs would produce higher ACVs. A secondary purpose was to examine the relationship between bar velocity during the 1RM back squat in relationship to measures of strength and speed to determine whether PCV may provide some useful information for coaches in stratifying athletes. We hypothesized that the PCV during the 1RM back squat would be positively related to relative back squat strength and negatively related to 36.6-m sprint time (a common assessment of speed for college athletes).
Experimental Approach to the Problem
This investigation used a cross-sectional study design. Subjects were asked to avoid strenuous exercise within 24 hours before their visit to the laboratory. In the laboratory, height, body mass, age, femur length, back squat training age, and current back squat training frequency were recorded. Subjects then performed two 36.6-m sprints followed by assessment of their 1RM back squat. The ACV and PCV were recorded during the 1RM assessment.
Thirteen football and 8 softball athletes from a National Association of Intercollegiate Athletics (NAIA)-affiliated University volunteered to participant in this study. All subjects were 18 years or older. Of the 13 football players, 8 were considered linemen (offensive or defensive) and 5 were considered skill position players (e.g., running back, quarterback, wide-receiver, tight end, linebacker, or defensive back). Of the 8 softball players, 5 were outfielders (left, center, or right field) and 3 were infielders (first base, second base, third base, pitcher, or catcher). All subjects were familiar with the 36.6-m sprint test and back squat as part of their team-related activities, and all subjects provided written informed consent after being informed of the risks and benefits of the procedures. This protocol was approved by Lindenwood University, Belleville (approval no. 00012).
Subjects standing height was recorded to the nearest 0.001 m with a standard stadiometer (Tanita HR-200; Arlington Heights, IL, USA). Body mass was recorded with an electronic scale (Tanita BWB-800S Doctors Scale) to the nearest 0.1 kg. With the participant in a seated position and the knee and hip joints flexed at approximately 90°, the length of each femur was measured with a tape as the distance from the greater trochanter to the lateral condyle of the femur and recorded to the nearest 0.001 m. The length of the right and left femurs were then averaged and used to represent the femur length. The intraclass correlation coefficient (2-way, mixed effects) for the femur length measurement was 0.957.
Subjects were asked to verbally indicate how many years of experience they have with the back squat to the nearest 0.5 years (training age) and how frequently they currently (i.e., in the last month) performed the back squat to the nearest 0.5 days per week (squat frequency).
Subjects completed a ∼5-minute self-selected dynamic warm-up including running and stretching befiore the 36.6-m sprint test as recommended (18). The sprint was performed on an outdoor football field. Subjects began in a 3- or 4-point stance and the time was started on the participant's first movement. All participants were familiar with the 36.6-m sprint test, and thus a familiarization session for this test was not incorporated. Two independent testers timed each trial using a stopwatch and their 2 times were averaged to the nearest 0.01 seconds for each trial. At least 2 minutes of rest was given between attempts, and the lower of the 2 trials was used for analysis.
One Repetition Maximum
Subjects were asked to indicate their estimated 1RM (e1RM) back squat from which the loads for all warm-up sets were determined. All subjects first completed 5–10 repetitions with an unloaded bar (20 kg) to ensure that proper depth was achieved during the movement. Proper back squat depth for the purposes of this study was a depth at which the crease of the hip was at or below the level of the top of the patella when viewed from the lateral aspect. Squat depth was judged by a Certified Strength and Conditioning Specialist. Taking a step with the bar during the ascent of descent of the squat resulted in the lift being judged unsuccessful, although elevation of the heel (without moving the entire foot) was allowed. Similar to the protocol used by Zourdos et al. (22), warm-up sets consisted of 8 repetitions with approximately 25% of the e1RM, 4 repetitions with approximately 50% of the e1RM, 3 repetitions with approximately 65% of the e1RM, and 1 repetition with 75 and 85% of the e1RM. The squat 1RM was then determined within 5 attempts and recorded as the greatest load lifted through a full range of motion. Only 1 subject was successful during all 5 potential 1RM attempts. Subjects were instructed to complete the concentric portion of each repetition with maximal intended acceleration, and verbal encouragement was provided during the test to ensure that the highest possible ACV and PCV were recorded for each repetition. Relative squat 1RM was calculated as the squat 1RM divided by body mass.
A Tendo Power and Speed Analyzer—PSA 310 Unit (TENDO FitroDyne) was placed next to the squat rack with the velcro strap affixed to the bar touching the inside of the bar sleeve so that the sensor unit cable was vertical in both the sagittal and frontal planes when the squatter was in the starting position (i.e., standing with full knee and hip extension). This device has been shown to be a reliable and valid instrument to assess velocity and power of barbell exercises (8). The ACV and PCV (m·s−1) was recorded for each repetition above 40% of the 1RM as previously recommended (16,21); when multiple repetitions were performed during warm-up sets, the repetition with the greatest ACV was recorded and used for analysis. This was done in accordance with previous research to establish the highest ACV at each load without inducing fatigue (15). Peak and average power (Watts) were calculated as the load lifted plus 88% of the subject's body mass (body mass excluding the estimated mass of the shank and foot) multiplied by acceleration due to gravity (9.81 m·s−2) multiplied by the peak or average velocity, respectively. This is the recommended method for calculating power from lower-body dynamic movements (5). Relative peak and average power (W·kg−1) were determined by dividing peak and average power by body mass, respectively.
All data were checked for normality using the Shapiro-Wilk test. All subject characteristic data were normally distributed except for squat frequency. Subject characteristics were compared between athletes (football vs. softball players) using independent samples t-tests for normally distributed data and independent samples Mann-Whitney U-Test for non-normally distributed data. Pearson product moment correlations were used to analyze the relationships between select variables. An alpha level of <0.05 was used to determine the statistical significance for all statistical tests. All data are presented as mean ± SD and analyzed using IBM SPSS (version 23).
All data were normally distributed except for squat frequency in which 11 of the 21 subjects indicated a squat frequency of once per week. Figure 1 presents the relationship between ACV values and load lifted for all warm-up sets and successful 1RM attempts at or above 40% 1RM for all subjects combined. As expected, there was an inverse relationship between ACV and the relative load; about 75% of the variance in ACV was explained by the load (Figure 1). On average, the football players were statistically older, taller, heavier, and had a greater squat 1RM than the softball players (p ≤ 0.05); velocity measures, power measures, and 36.6-m sprint times were not statistically different between softball and football players (p > 0.05; Table 1). Table 2 presents Pearson product moment correlation coefficients for selected variables. Neither femur length, training age, nor squat frequency was significantly related to ACV (p > 0.05). However, 36.6-m sprint time was inversely related to PCV (r = −0.612; p = 0.003), relative squat 1RM (r = −0.720; p < 0.001), and both squat average (r = −0.560; p < 0.001) and peak (r = −0.779; p < 0.001) relative power (Table 2).
The main finding of this study was that the ACV for the 1RM back squat was not related to either squat training age or femur length as hypothesized. In fact, ACV at 1RM varied nearly two-fold (0.22–0.43 m·s−1) among athletes who regularly engage in back squat training, but was not different between the male and female athletes. A second novel finding is the moderate-to-strong significant relationships observed between 36.6-m sprint time and PCV as well as relative back squat strength and power.
The mean ACV across all subjects was 0.30 ± 0.05 m·s−1 and there were no differences in ACV values between softball and football players. Squat 1RM ACV measured during the squat performed on a Smith machine is very similar (0.32 ± 0.03 and 0.31 ± 0.02 m·s−1) to the present investigation (16,21). These investigations (16,21) observed an even stronger relationship between load (%1RM) and back squat velocity (R2 = 0.96) when using the Smith machine which suggests that variability in free weight squat technique may contribute to the variance in ACV. Other studies which have measured the bar velocity during the free weight squat have shown more variability. For example, the ACV during the free weight 1RM squat has been reported in as low as 0.23 ± 0.05 m·s−1 in competitive powerlifters (12), 0.24 ± 0.04 m·s−1 in experienced lifters (22), 0.27 ± 0.02 m·s−1 in physically active subjects (13), and 0.34 ± 0.07 m·s−1 in novice squatters (22). Studies which have observed ACV variability in the free weight back squat have suggested that muscle morphological aspects (4), high relative loads (3), and usage of the stretch-shortening cycle (2) may all contribute to this between-subject variation in the load-velocity relationship. With this between-subject variability in velocity also observed in this study, our findings support the suggestion for individual velocity ranges used for VBT. However, contrary to our hypothesis, within this group of college athletes with various amounts of squat experience (between 1 and 10 years of experience), training age was not related to ACV. Although our sample of athletes had a range of training ages, the average training age was relatively high (6.5 ± 2.5 years), and it is possible that the relationship between training age and ACV is nonlinear in which ACV rapidly adapts (decreases) within a few weeks or months of training and then adapts more gradually thereafter. A second explanation for the lack of relationship between ACV and training age presently is that previous investigations (20,22) had all subjects as “experienced” or “novice,” whereas 19 of 21 subjects had at least 4 years of training experience; thus, despite an overall wide range, our sample was more homogenous in terms of training age. Future investigations including more athletes with lower training ages would further clarify this relationship.
The lack of a relationship between femur length and ACV also failed to support our initial hypothesis. In theory, taller lifters with longer limbs would achieve higher velocities during their movements because of a greater range of motion (7). However, the relationship observed between ACV and femur length (r = 0.002) was negligible. It is possible that other factors play a greater role in ACV, which obscures the relationship between femur length and ACV.
Collectively, these results combined with the previous literature suggest that velocity ranges for the squat may be unique between groups of individuals (e.g., nonathletes vs. athletes) and variables within a single group (2–4,22). Although the relative load (%1RM) explains the majority of the variance (∼75%) in ACV (Figure 1), relative strength level (1RM/body mass) was not related to ACV (r = −0.090). Similar to our findings, other studies have also concluded no effect of relative strength on ACV (16,21). In our investigation, there was still quite a bit of unexplained variance (∼25%) in ACV which may be due to factors such as muscle fiber–type, pennation angle, and moment arm length of agonist muscles and potentially variation in squatting technique (e.g., sagittal plane bar movement). Future studies may wish to investigate how technique as well as other biomechanical and physiological factors may influence ACV and ultimately recommendations for VBT.
Although most studies have used ACV as the primary velocity parameter for prescribing training (9,10) or predicting the 1RM (14,15), less research has investigated the peak velocity attained during a movement. We observed relatively strong relationships between the athletes' PCV during the back squat and their 36.6-m sprint time as well as their relative power outputs. Because many team sports have a need for development (and assessment) of both speed and power, the use of PCV measurements during back squat strength testing may be useful for estimating these parameters in athletes without needing to perform multiple tests. We suggest that PCV, rather than ACV, during the 1RM back squat showed a stronger relationship to 36.6-m sprint time because it may be more reflective of the athlete's ability to achieve a high (peak) velocity while exerting high forces which is similar to generating the rapid, high forces needed during a 36.6-m sprint. Previous work has shown the relationships between isokinetic peak torque values and sprint performance to be greater at higher angular velocities compared with lower angular velocities (19). Another factor which may influence the magnitude of the relationship between PCV and 36.6-m sprint time is the range of PCV values which was much greater (0.44–1.06 m·s−1) compared with the range of ACV values observed (0.22–0.43 m·s−1). The sprint time was inversely related to relative squat strength suggesting that relative strength in the lower body has some influence on speed capabilities (19).
Our study is limited by the sample size of 21 athletes. It is possible that our subjects overestimated their training age if they only trained for part of a year but considered that a full year of training experience. Another limitation of our study was the lack of standardization in the warm-up for the sprint test. Similar studies have used larger samples but also excluded female athletes (16,21). In contrast, our sample included both male and female athletes who exhibited a wide range of squat experience, limb lengths, squat strength, and speed. In addition, our study used the free weight back squat which may make the relationship between ACV and load more variable, but also provide more practical applicability for coaches using VBT for free weight exercise. Future studies with larger groups of sport athletes (both men and women) would be needed to develop athlete-specific velocity ranges for use in prescribing training based on velocity for various free weight exercises. Another consideration when interpreting ACV and PCV values during the back squat is the phase during which PCV is attained. Specifically, ACV includes the entire concentric portion of the repetition because that is the recommended velocity parameter used for prescribing training load and estimating a 1RM (15). On the other hand, PCV is likely attained at some point after the “sticking region” of the concentric portion of the squat, and this point may vary between individuals.
These results suggest that college athletes using velocity to regulate squat training may not necessarily need to modify velocity ranges based on the limb length or training age. However, velocity ranges still should be individualized. Furthermore, until the individual factors affecting ACV can be fully elucidated, coaches and athletes can use the rating of perceived exertion scale along with velocity as a practical method to individualize training loads. Peak velocity during a 1RM back squat may be a useful indicator of an athlete's relative power output ability and speed. Coaches may consider measuring velocity during strength testing as a surrogate measure for speed and power.
In summary, we observed nearly a two-fold range in ACV during a 1RM back squat in collegiate football and softball players. However, neither training experience nor femur length were related to the ACV or PCV. In addition, PCV during a 1RM back squat may be useful as an indicator of speed and power in college athletes. Future research should aim to further elucidate the factors which impact ACV.
The authors thank Michal Carter, Kendall Taylor, Ty Poore, Michael Williams, and Joel Reyna for their work in data collection.
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