The Impact of Different Velocity Loss on Post-Activation Performance Enhancement (PAPE) Effects in Sprint Athletes

1. Introduction

Pre-event warm-up activities are crucial for enhancing athletes' performance as they can elevate body temperature, increase the speed of neural impulse conduction, and alleviate pre-competition anxiety[1]. Post-activation performance enhancement (PAPE) is an effective warm-up protocol that can improve athletic performance. PAPE refers to a physiological phenomenon where brief high-intensity resistance training conducted beforehand leads to an increase in muscle explosiveness and force generation speed [2]. Unlike the classic post-activation potentiation (PAP) effect, the enhancement effect of PAPE on muscle strength can last for several minutes after the activity, whereas the effect of PAP reaches its maximum shortly after the activity, approximately around 28 seconds [3]. Unlike PAP, the mechanism of PAPE may involve factors such as muscle temperature, muscle/cellular water content, and muscle activation [3,4]. The post-activation enhancement effect has also been applied to various other sports to enhance athletic performance. In pre-event warm-up activities for sprinters, using a 10% load can be more effective in improving the 20m sprint performance of elite female sprinters compared to schemes incorporating resistance training with 5% or 15% of body weight[5]. Interventions involving squats with either 90% 1RM for 3 repetitions or 60% 1RM for 6 repetitions can lead to a slight increase in mean power output for sprinters, although there is no statistically significant difference between the two different loads. Additionally, squat schemes with elastic bands can also improve sprinters' performance [6].

In the current formulation of protocols inducing PAPE effects, most studies have used PAPE training schemes, such as those based on the maximum number of repetitions (Repetition Maximum, RM), to determine the intensity of the load, which suffer from the problem of inability to accurately control intensity [7,8]. These loading schemes (e.g., 90% 1RM squats) may overlook the individual differences in physical function status among different athletes. The fundamental issue with these differences lies in the fact that the selected load intensity (such as RM) cannot accurately reflect the neuromuscular status of the athletes. Factors such as athletes' nutrition, sleep, and fatigue status can affect their neuromuscular status and athletic performance [9]. Therefore, to better design loading schemes for PAPE, it is necessary to further optimize these schemes.

In strength training, as muscle fatigue accumulates, the execution speed of movements may decrease. Utilizing movement speed to adjust or formulate training loads is a potential approach to address the issue of precise load monitoring [9]. Velocity loss (VL), which refers to the percentage decrease in movement speed relative to the initial speed, is a commonly used indicator to quantify movement speed. Studies have found a high correlation between 1RM and movement speed in different strength training exercises (r=0.998) [10]. Research analyzing the load-velocity relationship in exercises such as push-ups, pull-ups, half squats, full squats, and leg presses has also revealed a strong correlation between load magnitude and bar speed (equation predictive capacity R² = 0.96-0.98), which is independent of training background and athletes' strength levels [11]. Therefore, in addressing the issue of objectively quantifying and monitoring athletes' actual training loads, it is suggested to monitor the load based on the magnitude of VL achieved in each set of strength training, rather than being limited to fixed repetitions prescribed by relative loads (%1RM). Additionally, monitoring VL load can not only ensure accurate matching of target intensity with actual training intensity but also control the level of fatigue during each set, avoiding excessive fatigue and achieving consistency in stimulus levels among individuals [12,13].

Regarding the selection range of VL, studies have found that 20% VL is a critical value, where VL lower than 20% is more conducive to rapid strength improvement, while VL exceeding 20% is more conducive to muscle hypertrophy (20%-40% VL), and when the difference in VL is within 10%, there seems to be no significant difference in training effects [14]. However, there is considerable controversy surrounding the critical value of 20% VL in many studies. Some research suggests that compared to 20% VL, strength training with 10% VL is more beneficial for explosiveness and muscle strength gains [15]. Meanwhile, studies have also compared the relationship between 10% VL and 20% VL with a 15m sprint time and found that both VLs achieve similar effects [16]. So far, there is no evidence to suggest that the effect of velocity loss-induced PAPE is necessarily superior to traditional load-based RM schemes, which requires further exploration.

Therefore, this study, with elite sprinters as participants, sets two velocity loss load intensities of 20% VL and 10% VL as the experimental groups, and traditional %1RM load intensity as the control group, aims to investigate the effects of different velocity loss (10% VL, 20% VL) on sprinters' PAPE. The goal is to provide new insights into the personalized load adjustment of PAPE training programs in the daily training and pre-competition preparation activities of sprinters. It was hypothesized that (1) training programs based on velocity loss (VL) load intensity and traditional %1RM load intensity can both improve sprint performance, and (2) stride length and stride frequency are potential factors influencing sprint performance.

2. Materials and Methods

 

20. m Sprint Test

The Smartspeed segmented timing real-time feedback system (Smartspeed, Australia) was utilized to collect the time taken by the participants for the 10m and 20m sprints. During the test, participants adopted a fixed spike shoe and a crouching start position. To ensure the accuracy of the test, participants recorded the distance between their feet and the angle of the force plate under their feet before each test. To prevent premature contact with the infrared sensor, the support points for the participants' hands were set at a certain distance behind the instrument. At the start of the test, participants indicated their readiness to the tester, and the experimenter manually clicked the start button upon hearing the "beep" sound. Participants then commenced the sprint. As participants passed each segment of infrared emitted by the transmitter to the receiver, the sprint data collection was completed.

3. Results

Effects of Different Velocity Loss on PAPE in the 0-10m Phase

Following the intervention, a repeated measures analysis of variance revealed no significant interaction between the time effect and group intervention effect for the 0-10m sprint performance (F = 2.223, p = 0.083, partial η² = 0.526). Time main effect analysis indicated that the sprint times for the 0-10m phase at each testing point (4 minutes, 8 minutes, 12 minutes, 16 minutes) did not significantly decrease across the three groups (F = 1.474, p = 0.247, partial η² = 0.228). The main effect of group intervention showed no statistically significant differences in the sprint times for the 0-10m phase among the three groups at each testing point (F = 1.931, p = 0.169, partial η² = 0.149) (Figure 2A).

Subsequent effect size analysis revealed that the effect sizes for the TG group at each testing point were 0.081, -0.270, 0, and 0.081, respectively. For the 10%VL group, the effect sizes at each testing point were 0.202, -0.038, -0.038, and -0.071, respectively. As for the 20%VL group, the effect sizes at each testing point were -0.289, -0.279, -0.126, and -0.261, respectively (Figure 2B).

Effects of Different Velocity Loss on PAPE in the 10-20m Phase

Following the intervention, a repeated measures analysis of variance revealed no significant interaction between the time effect and group intervention effect for the 10-20m sprint performance (F=0.969, p=0.493, partial η²=0.326). Time main effect analysis indicated that the sprint times for the 10-20m phase at each testing point (4 minutes, 8 minutes, 12 minutes, 16 minutes) did not significantly decrease across the three groups (F=1.539, p=0.237, partial η²=0.123). The main effect of group intervention showed no statistically significant differences in the sprint times for the 10-20m phase among the three groups at each testing point (F=0.903, p=0.481, partial η²=0.153) (Figure 3A).

Subsequent effect size analysis revealed that the effect sizes for the TG group at each testing point were -0.204, -0.056, -0.241, and -0.167, respectively. For the 10%VL group, the effect sizes at each testing point were 0.067, 0, -0.107, and 0.053, respectively. As for the 20%VL group, the effect sizes at each testing point were -0.079, 0.095, 0.032, and 0.048, respectively (Figure 3B).

Effects of Different Velocity Loss on PAPE in the 0-20m Phase

Following the intervention, a repeated measures analysis of variance revealed a significant interaction between the time effect and group intervention effect for the 0-20m sprint performance (F=2.817, p=0.037, partial η²=0.585). Further analysis indicated that the 20%VL group exhibited significant differences at 4 minutes (p<0.05). Regarding the main effect of time, participants across all three groups did not show significant decreases in sprint times for the 0-20m phase at each testing point (F=0.852, p=0.509, partial η²=1.146). Similarly, there were no statistically significant differences in sprint times for the 0-20m phase among the three groups at each testing point for the main effect of group intervention (F=1.687, p=0.208, partial η²=0.133) (Figure 4A).

The calculated Cohen's d values for individual effect sizes are as follows: For the TG group, the effect sizes at each testing point were 0.188, 0.038, 0.05, and 0. For the 10%VL group, the effect sizes were 0.051, 0.071, -0.184, and -0.296, respectively. As for the 20%VL group, the effect sizes were -0.286, -0.263, -0.180, and -0.226, respectively (Figure 4B).

Impact of Different Velocity Loss on Stride Length in the 0-20m Phase

The results of repeated measures analysis of variance indicated that there was no significant interaction between the time effect and group intervention effect for stride length in the 0-20m phase (F=1.065, p=0.388, partial η²=0.034). Regarding the main effect of time, participants across all three groups did not exhibit significant changes in stride length for the 0-20m phase at each time point (F=2.282, p=0.061, partial η²=0.036). Similarly, for the main effect of group intervention, there were no significant differences in stride length among the different intervention groups at each time point (F=1.856, p=0.159, partial η²=0.015) (Figure 5A).

Individual effect sizes calculated using Cohen's d values revealed that for the TG group, the effect sizes at each testing point were -0.021, -0.002, -0.007, and 0.016, respectively. For the 10%VL group, the effect sizes were -0.016, 0.015, 0.030, and 0.027, respectively. As for the 20%VL group, the effect sizes were 0.022, 0.028, 0.037, and 0.012, respectively (Figure 5B).

Impact of Different Velocity Loss on Stride Frequency in the 20m Phase

The results of repeated measures analysis of variance revealed no significant interaction between the time effect and group intervention effect for stride frequency in the 0-20m phase (F=0.392, p=0.924, partial η²=0.013). Regarding the main effect of time, participants across all three groups did not exhibit significant changes in stride frequency for the 0-20m phase at each time point (F=0.625, p=0.645, partial η²=0.010). Similarly, for the main effect of group intervention, there were no significant differences in stride frequency among the different intervention groups at each time point (F=1.924, p=0.148, partial η²=0.015) (Figure 6A).

Individual effect sizes calculated using Cohen's d values showed that for the TG group, the effect sizes at each testing point were -0.013, -0.066, -0.031, and 0.006, respectively. For the 10%VL group, the effect sizes were -0.010, -0.010, 0.037, and 0.070, respectively. Notably, the 20%VL group exhibited effect sizes of 0.068, 0.028, 0.039, and 0.085 at each testing point (Figure 6B).

4. Discussion

The purpose of this study is to investigate the effects of different velocity losses on inducing PAPE in sprint athletes. The study primarily revealed that a 20% VL induces PAPE more effectively than a 10%VL. When employing a 20% VL warm-up, a significant improvement in sprint performance in the 0-20m phase was observed at the 4-minute mark. Although no significant differences were observed at other time intervals (8, 12, and 16 minutes), the time taken for both the 0-10m and 0-20m sprints was lower when using the 20%VL warm-up compared to the 10%VL and 1RM-based warm-up activities. This finding provides valuable insight for sprinters, suggesting that incorporating velocity-based warm-up protocols may replace traditional 1RM-based PAPE approaches, thereby addressing the limitation of traditional training methods in accurately controlling training loads based on individual states. A 20%VL warm-up may represent an optimal load for inducing PAPE in sprinters, enhancing their ability to initiate and accelerate during sprints. Due to the lack of significant changes in the data and considering the effect size, we believe that the results partially support our hypothesis.

The purpose of PAPE is to optimize an athlete's physical state before engaging in sports activities, aiming to enhance overall athletic performance. PAPE is commonly believed to improve explosive muscle capabilities, as observed in activities like vertical jumps and sprints, leading to more powerful and rapid muscle contractions, thereby elevating an athlete's explosive performance [17]. It is generally accepted that the potential effect can be induced as long as the minimum intensity and sufficient rest time are provided. While the intensity of 65% 1RM is sufficient to elicit a potentiation effect, intensities ranging from 85% to 90% 1RM can yield even greater results [18].

In this study, the use of a 20% VL significantly improved sprint performance in the 0-20m phase at the 4-minute mark. This suggests that performing squats with a 20% VL, followed by sprinting after 4 minutes, maximizes the PAPE effect. Similar findings have been reported in research involving sprinters, where a study focusing on sprinters revealed that PAPE induced by 90% 1RM significantly improved arrowhead agility test performance (p < 0.001) and repeated sprint ability (p = 0.002), with a shorter 20m sprint time post-PAPE (p = 0.005), demonstrating the effectiveness of PAPE on sprint performance [19].

In the context of long-term training, a study based on Velocity-Based Training (VBT) indicated that, despite the 5% VL group having only 32.6% of the repetitions compared to the 20% VL group, after 7 weeks of VBT training, both groups showed similar gains in strength, jumping, and sprinting performance [20].

Following intervention with 20% VL at other time points (8 minutes, 12 minutes, and 16 minutes), there were no significant differences in athletic performance, indicating a time-dependent occurrence of PAPE. A study inducing PAPE through Barbell Hip Thrusts (BHT) found that at 15 seconds, only 85% 1RM load induced PAPE, while the 50% 1RM load did not significantly impact performance. However, both PAPE-inducing protocols resulted in significant improvements in 10m and 15m sprints at 4 minutes and 8 minutes [21]. After a pre-stimulus of 91% 1RM, a notable improvement in sprint performance was observed compared to baseline, characterized by the best sprint time within 8 minutes [22].

Furthermore, the occurrence of PAPE may be influenced by the evaluation metrics. In a meta-analysis examining the acute effects of rest intervals on jump performance, it was found that intervals within 0-3 minutes had an adverse impact, while the range of 8-12 minutes positively influenced jump height [23]. Another meta-analysis reported that, for power output, a rest interval of 7-10 minutes was deemed the most ideal for inducing PAPE effects (ES = 0.70) [24].

In this study, not all load stimuli resulted in performance gains. Specifically, both traditional squat training based on 1RM load intensity and squat training with a 10% VL load intensity were found to be ineffective in enhancing performance in the 0-10m, 10-20m, and 0-20m phases. Although it has been suggested that a load intensity of 65% 1RM can induce a potentiation effect, some studies still report negative outcomes. For instance, Reardon et al. found that moderate and high-intensity schemes (3 sets of 10 repetitions or 3 repetitions, at 75% 1RM and 90% 1RM, respectively) did not improve performance scores, despite inducing acute muscle structural changes [25]. Several reasons could explain this phenomenon, such as considerable variability in individual responses to pre-activation stimuli, as well as the nature, intensity, and duration of the stimuli, all of which may influence the occurrence of PAPE. Additionally, the pre-activation state of individuals, including fatigue level, psychological state, and nutritional status, may impact the manifestation of the PAPE effect [3]. In the context of this study, neither the 85% 1RM nor the 10% VL load intensity elicited a PAPE effect. It is plausible to suggest that highly trained athletes, due to their extensive professional training, may have adapted their neuromuscular systems to typical pre-activation stimuli. This adaptation could potentially result in their neural systems being less responsive to stimuli that induce PAPE.

Several factors influence Post-Activation Potentiation (PAPE), including interindividual variability, pre-activation methods, recovery time, and the type of training. From an anatomical and physiological perspective, research suggests that PAPE is linked to increased calcium ion sensitivity in muscle cells, enhanced recruitment capacity of fast muscle fiber motor units, and alterations in the pennation angle during muscle contraction [26]. Studies indicate that enhancing the sensitivity of myosin and actin-binding sites to calcium ions and increasing the influx of calcium ions into the sarcoplasmic reticulum, requires rapid muscle contraction [27]. Pre-activating muscle contraction may increase the release of neurotransmitters, enhance the efficiency of neurotransmitter transmission, or reduce the likelihood of failure in action potential transmission at axon branch points, thereby increasing the recruitment of fast muscle fiber units [14,28]. Additionally, changes in the pennation angle of muscle fibers under 20% VL resistance activation are conducive to transferring force to relevant tendons, thereby improving the mechanical efficiency of muscle fibers [29]. Santanielo et al. found that the pennation angle increased to some extent after non-exhaustive strength training loads that approximated 20% VL [30].

In the context of a 100m sprint, both stride length and stride frequency can impact performance. Stride length can be trained through methods such as improving flexibility, enhancing technique, and exerting more force on the ground, while stride frequency training focuses more on the central nervous system's reaction time [31]. In theory, PAPE may enhance muscle strength and central nervous system reactivity, thus influencing stride length and frequency. Changes in stride length and frequency could ultimately be the reasons behind variations in sprint performance. Therefore, this study attempts to explain the reasons for PAPE from the perspectives of stride length and frequency. Changes in stride length and frequency before and after the intervention were tested, but no significant alterations were observed. Looking at the trend, at the 8-minute and 12-minute stages, the 20% VL group showed slightly higher stride lengths than the 10% VL group and TG group, while their stride frequency was slightly lower than that of the 10% VL group and TG group. Some studies suggest that in a 100m sprint, stride length contributes more significantly to speed than stride frequency. Excessive acceleration (correlated with high stride frequency) during rapid strides after the start negatively affects speed throughout the entire 100m sprint. Therefore, it is necessary to control stride frequency and increase stride length in the initial stages of rapid strides after leaving the starting line to reduce or eliminate the negative impact of excessive acceleration [32]. This to some extent explains why the 20% VL group in this study exhibited a trend of better performance than the 10% VL group and TG group.

5. Conclusions

Compared to traditional PAPE protocols based on 1RM, the velocity-based PAPE protocol can more effectively induce PAPE effects in sprinters. While PAPE is a highly individualized phenomenon, optimal rest intervals may be influenced by individual factors such as fast-twitch fiber composition and strength levels. However, considering practical convenience and applicability, utilizing a 20% VL load intensity with a 4-minute rest interval appears to yield the best PAPE outcomes.