The term “postactivation potentiation” (PAP) refers to the phenomenon that significantly enhances muscular power and, consequently, performance as a result of previous muscular work (8,34). The PAP phenomenon is induced by a voluntary contraction, as conditioning activity (CA) and has been shown to increase power during subsequent contractions of the muscle fibers (2,25). In general, short-term gains on muscle performance after CA are thought to include phosphorylation of myosin regulatory light chains and increased recruitment of higher order motor units (2). After a CA protocol, mechanisms of muscular fatigue and PAP coexist, and so, subsequent power output and performance depend on the balance between these 2 factors.
The efficacy by which a CA can stimulate PAP mechanisms and acutely enhance muscular performance ultimately depends on several factors (7), including, but not limited to, training experience (18), rest period length (19,31), and the type, intensity and volume of the CA performed (30). These variables have not been standardized in past research, and as a result, evidence of the effects of CA on the performance of subsequent explosive activities is equivocal (24) and no precise consensus has been formed regarding the optimal acute conditioning mode protocol in recreationally training and athletic populations (34).
In general, PAP is expected to occur after evoked contractions and after near-maximum or maximum voluntary CA in power-trained athletes when performing explosive tasks, but after a run to fatigue, power performance is not supposed to improve (23). Nevertheless, previous studies indicate that not only explosive, short, and intense stimuli can be used as a CA but also submaximum and longer or prolonged exercises can cause PAP for subsequent activities (4,5,12,22,32). Vuorimaa et al. (33) reported changes in coordination strategy in leg extension exercises performed after induced fatigue by long-distance running in elite athletes, suggesting a link between endurance training and PAP. Hamada et al. (16) indicated that endurance training causes, on one hand, a greater amount of phosphorylation of regulatory myosin light chains in slow fibers, and on the other hand, a greater resistance to fatigue, which would allow the prevalence of potentiation, and it would explain the PAP presence in endurance athletes. Similarly, twitch potentiation has also been observed in endurance-trained athletes in evoked contractions after continuous (4,5,22) and intermittent running bouts (33). Moreover, PAP has also been reported in endurance-trained athletes in jump performance after intermittent exercises (33), continuous running exercises (12,33), and incremental protocols (4,22,32).
Considering these previous studies, the effects of specific fatigue induced by running exercises on power performance and rapid and explosive force have been studied in long-distance runners. However, most of the studies have been conducted in laboratory or maximum field tests. To the best of the researchers' knowledge, no study exists that assesses induced fatigue with a common workload in endurance athletes (extended interval training, EIT), that is performed in an outdoor track (field test), and that shows effects on power and rapid force across vertical jump and handgrip strength performance. Therefore, the aims of this study were (a) to analyze acute effect of running exercises (EIT) on countermovement jump (CMJ) and handgrip strength in endurance athletes and (b) to determine the relationship between fatigue and potentiation in long-distance runners during EIT.
Experimental Approach to the Problem
This study analyzes the evolution of CMJ and handgrip strength performance in distance runners in an EIT. This allows a comparison to be done between performance at rest (unfatigued condition) and at different levels of exercise-induced accumulated fatigue (fatigued condition). Different parameters such as the rate of perceived exertion (RPE), peak heart rate (HRpeak), heart rate recovery (HRrec) and accumulated lactate were assessed. These parameters are monitored in recovery from the EIT.
Thirty experienced recreational male long-distance runners, with a minimum experience of 6 years of training and competition (age range = 18–40 years old, mean age = 28.26 years, body mass index [BMI] = 22.24 ± 2.50 kg·m−2, and maximal oxygen consumption [
] = 58.7 ± 4.50 ml·kg−1·min−1), participated voluntarily in this study. More information about participants is shown in Table 1. The study was conducted at the end of season 2012–13. The athletes trained regularly and had no history of injury in the 3 months before the study, which would limit training. After receiving detailed information on the objectives and procedure of the study, each subject signed an informed consent form to participate, which complied with the ethical standards of the World Medical Association Declaration of Helsinki (2008), and which made it clear that they were free to leave the study if they saw fit. The study was approved by the Ethics Committee from the University of Jaen (Spain) and was conducted following the European Community's guidelines for Good Clinical Practice (111/3976/88 of July 1990) and the Spanish legal framework for clinical research on humans (Real Decreto 561/1993 on clinical trials).
Subjects were tested individually on 2 occasions. First, in a preliminary session, an anthropometric assessment and familiarization with CMJ and handgrip strength test were carried out. Anthropometric parameters were as follows: height (in meters) measured with a stadiometer (Seca 22; SECA Corp., Hamburg, Germany), body mass (in kilograms) recorded with a Seca 634, and the BMI (body mass [in kilograms]/height [in square meter]). Within the first session, the Léger's test was performed, through which
could be estimated (20), and which consisted of 20-m sprints with increasing speed in each run, indicating the pace with audible signals.
was calculated based on the speed that the participant reached in the last sprint through the following equation (20):
(in milliliter per kilogram per minute) = 5.857 × velocity (in kilometer per hour) − 19.458.
The second session was performed 7 days after the preliminary session on an outdoor running track (lane 1) (temperature = 17.16 ± 5.81° C, relative air humidity = 62.65 ± 16.06%). Subjects were instructed to avoid strenuous exercise 72 hours before the training protocol. Before EIT, the athletes performed a warm-up, which consisted of 5–10 minutes of continuous running at a comfortable speed and 10 minutes of general exercises (high skipping, leg flexions, jumping exercises, and short bursts of acceleration). Then (pretest, unfatigued condition), the participants did 3 CMJs, separated by a 15-second recovery, which were then averaged, and 2 attempts of a 3-second handgrip, separated by 15 seconds of recovery, and the average of both hands was calculated. Next, the participants began the EIT protocol, which consisted of 12 runs of 400 m, grouped into 4 sets of 3 runs, with a passive recovery of 1 minute between runs and 3 minutes between sets (4 × 3 × 400 m) (Figure 1). Interval training widespread: it is used in the physical preparation of almost all endurance athletes (15,33) and is characterized by efforts lasting from 60 to 90 seconds with an intensity of 85–100% of maximal aerobic speed and with a high volume of bouts.
Between each 400-m run, the RPE on the Borg Scale (3) was recorded together with HRpeak and HRrec, using the Garmin Forerunner monitor 405 (Garmin International Inc., Olathe, KS, USA), time spent in each 400-m run (T400m), and the handgrip strength of both hands (under identical conditions as those in the pretest). Moreover, between each set, the CMJ and blood lactate were also measured. The CMJ performance was recorded 2 minutes after the end of the last conditioning stimulus (the last 400 m of each set), obtaining consequently the CMJ performance in fatigued condition (set 1, set 2, set 3, and set 4). The CMJ was performed under identical conditions to the pretest (3 jumps separated by 15 seconds, and the average of the 3 was calculated). Blood lactate was recorded after the last run of each set, and for this purpose, a portable lactate analyzer Lactate-Pro (blood lactate in millimoles per liter; Arkray, Inc., Kyoto, Japan) was used. The measured time used in each set was the average of the 3 runs (T400m).
During CMJ, the subjects were required to flex their knees to a 90° angle. Participants are experienced athletes who perform CMJ in their daily training sessions. Moreover, to make sure the execution of the CMJ is correct, a familiarization session was carried out previously. The CMJ was recorded using the FreePower Jump Sensorize device (FreePower®, Sensorize srl, Rome, Italy), which was previously validated (26), and follows the following parameters (averaged from the 3 trials): maximum height of jump (in centimeter), peak force (Pforce; in Newton per kilogram), peak power (Ppower; in Watt per kilogram), eccentric work (EccW; in joule per kilogram), and concentric work (ConcW; in joule per kilogram). External mechanical work has been calculated using the variation of instantaneous total mechanical energy of the center of mass (6).
The handgrip strength test was performed considering the recommendations of previous studies (30). To record the handgrip strength (in kilogram), a digital hand dynamometer was used (TKK 5101 Grip D; Takey, Tokyo, Japan), adjusting the optimum grip through the calibration formula of Ruiz et al. (29). Participants were encouraged to achieve maximum handgrip strength.
The data were analyzed by the statistics program SPSS version 19.0 for Windows (SPSS, Inc., Chicago, IL, USA) and the significance level was set at p ≤ 0.05. The data are shown as descriptive statistics of mean and SD. The researchers used the Shapiro-Wilk test to verify normal distribution of data. The comparison of data between measures (at rest [pretest], set 1, set 2, set 3, and set 4) for the entire group was performed using a repeated-measures analysis (analysis of variance [ANOVA]) with post hoc Bonferroni's test. The researchers then performed the nonparametric contrast test of Friedman and Wilcoxon for those data in which no normal distribution was achieved after several transformations (square root transformation and logarithmic). Pearson's correlation between the increase from rest to set 4 and between set 1 and set 4 was used. Finally, the researchers performed a cluster analysis (k-means) grouped according to whether PAP was experienced (RG, responders group, n = 17) or not (NRG, nonresponders group, n = 13) in relation to CMJ change (ΔCMJ) from rest condition to postexercise accumulated fatigue (at the end of protocol, set 4). An analysis of covariance (ANCOVA) was performed between groups (RG and NRG) in all analyzed variables in nonfatigued condition (at rest in CMJ and handgrip strength, and set 1 for the rest of variables), and in posttest (fatigued condition at the end of EIT, set 4). In both analyses, covariables of age and BMI were considered. Also, ANCOVA was performed in post-pre difference (Δ), using the pretest as a covariable. The reliabilities of vertical jump (CMJ), handgrip strength, and blood lactate levels were assessed using intraclass correlation coefficients (ICCs) between test-retest and confidence interval (CI).
Test-retest reliability analysis of physical and physiological tests in the present study shows an ICC of 0.986 (95% CI = 0.972–0.993) for the CMJ, 0.963 (95% CI = 0.927–0.981) for the handgrip strength, and 0.974 (95% CI = 0.914–0.992) for blood lactate levels.
Cluster analysis was able to show the differences between those participants who experienced a significant level of PAP (p < 0.001) in the CMJ during the entire exercise session (RG; n = 17) and those participants who did not experience PAP (p ≥ 0.05) in the CMJ (NRG; n = 13). The ANOVA performed between the 2 groups (RG and NRG) shows no significant differences (p ≥ 0.05) in BMI (RG = 22.01 ± 2.39 kg·m−2, NRG = 22.65 ± 2.82 kg·m−2),
(RG = 56.60 ± 3.24 ml·kg−1·min−1, NRG = 57.40 ± 3.10 ml·kg−1·min−1), or age (RG = 27.05 ± 7.22 years, NRG = 30.15 ± 9.92 years). Considering the difference in CMJ performance between rest condition and fatigued condition (from rest to the set 4, Δ), the ANCOVA revealed significant differences in ΔCMJ between both groups (p < 0.001), which produces an increase in RG (+5 cm, 13.89%) while NRG remained unchanged (Figure 2). In addition, significant differences (p ≤ 0.05) are shown between RG and NRG in ΔConcW and ΔHandgrip strength, with post-pre differences of +0.29 and −0.29 J·kg−1 (4.66 and −4.6%, respectively) for ConcW and +1.55 and −1.05 kg (+3.82 and −2.42%, respectively) for handgrip strength. Finally, T400m shows no significant difference between both RG and NRG (Table 2).
Table 3 shows the obtained results in repeated measures (ANOVA) for each one of the parameters used as fatigue parameters (Lactate, HRpeak, HRrec, and RPE). A significant increase (p < 0.01) in the registered values of each one of the above-mentioned parameters could be seen throughout the protocol. This increase exists not only in the entire group but also when observing the individual results of RG and NRG. No significant differences (p ≥ 0.05) between RG and NRG were shown between set 1 and set 4, either in unfatigued or fatigued condition or between sets, except for RPE in set 1 (p ≤ 0.05).
Table 4 presents obtained results in different repeated measures (ANOVA) for each one of the performance parameters assessed (T400m, CMJ, Pforce, Ppower, ConcW, EccW, and handgrip strength) and post hoc Bonferroni's comparison intragroup to find significant differences in pairs of measures. Considering the entire group, significant changes were observed (p < 0.001) in CMJ, Pforce, and Ppower. Significant changes were seen for RG, T400m (p = 0.049), CMJ (p < 0.001), Pforce (p = 0.001), Ppower (p = 0.001), and handgrip strength (p = 0.005). In contrast, for NRG only ConcW (p = 0.033) and handgrip strength (p = 0.042) changed significantly. The polynomial contrasts demonstrate that CMJ (p = 0.001), Pforce (p < 0.001), and Ppower (p < 0.001) are adjusted to linear function. This connection can be seen increasing during the training session. Also, an adjustment to quadratic function in handgrip strength (p = 0.003) was found.
For all participants involved (n = 30), the Pearson's correlation analysis between the different CMJ measures and the rest of analyzed variables was performed. Some significant correlations were found: CMJ with Ppower and CMJ with ConcW (p < 0.01) throughout the entire session (values for r, rest = 0.663 and 0.714; set 1 = 0.620 and 0.760; set 2 = 0.708 and 0.785; set 3 = 0.525 and 0.808; set 4 = 0.687 and 0.830, respectively). Also, a significant correlation was found between ΔCMJ with ΔHandgrip strength (r = 0.375, p = 0.041) (Figure 3).
Focusing on the created groups through cluster analysis (RG and NRG), it is convenient to note the correlation between ΔCMJ-Lactate and ConcW-Lactate. For RG, a positive correlation was found for both (r = 0.535, p = 0.027 and r = 0.531, p = 0.028, respectively), whereas for NRG, a negative correlation was found for both (r = −0.782, p = 0.002 and r = −0.767, p = 0.006, respectively).
The main finding of this investigation is the presence of PAP in CMJ of long-distance runners, during a field study based on classic intermittent training (EIT), a very common protocol for endurance athletes. However, the novelty of this study is not that the entire group experienced PAP, but rather that despite accumulated fatigued brought about by exercise and the fact that all athletes involved had similar characteristics—no significant differences (p ≥ 0.05) concerning BMI,
, and age—only some of the athletes boosted their performance in the CMJ during the protocol (RG). The performance of others remained unchanged (NRG). Moreover, the athletes performed the training protocol according to the criteria of intensity required. The evolutions of the descriptive parameters of fatigue indicate its increase throughout training protocol, reaching very high intensity levels in each one of them (RPE: 18.36 ± 0.97; HRpeak: 182.20 ± 9.62; HRrec: 155.43 ± 13.07; and blood lactate: 13.55 ± 2.41) and with no significant differences between RG and NRG. This fact eliminates and negates the possibility that PAP could be caused by athletes' level of involvement.
Previous studies have demonstrated PAP in different conditions. There is an abundance of literature on the subject of PAP in sprint or vertical jump using exercises with external loads or resistance like CA (6,16,32,34). However, to the best of the researchers' knowledge to date, a limited number of studies have investigated running exercises to elicit PAP in explosive movements such as CMJ. Boullosa and Tuimil (4) and Boullosa et al. (5) showed PAP in CMJ after incremental field running test. Vuorimaa et al. (33) found PAP in CMJ after 3 different running exercises (intermittent, continuous, and until fatigue), although tests were performed on a treadmill. Therefore, current research is novel because of its focus on PAP phenomenon in specific abilities like vertical jump, during actual and widespread field training session.
Looking at the data obtained for the entire group, PAP in CMJ was produced (+3 cm, 7.89%; p < 0.001) based on significant improvement in Ppower and Pforce (p < 0.001), whereas the rest of mechanical parameters associated to CMJ, like EccW and ConcW, remained unchanged (p ≥ 0.05). Focusing on CMJ improvement, controversial data were found in similar studies. Boullosa et al. (5) reported an improvement of 4.9% in CMJ after the incremental running test while Boullosa and Tuimil (4) pointed out an increase of 12.7% in a similar, previous study. Vuorimaa et al. (33) obtained an improvement very similar to this study's data +8.9% in CMJ. Just as Boullosa et al. (5) indicated, it is difficult to compare results with previous studies because of the influence of the method used in PAP magnitude. Regarding mechanical parameters, the obtained results in the present study confirm the idea of PAP as a measure of CMJ performance and is therefore highly related to Ppower (5). In this study, the significant correlations found between CMJ-Ppower throughout the entire session support this rationale.
As for the rest of assessed parameters in this study, no significant changes were produced (p ≥ 0.05) nor in the handgrip strength or in the T400m, despite induced fatigue for the training protocol, as mentioned previously. Considering the results obtained by the entire group, the maintenance of T400m despite high values of fatigue registered would be another indicator of high involvement in the participants. The maintenance of handgrip strength, even showing a trend to increase indicates the importance of central mechanisms in maintaining a certain level of force (9). Central fatigue induced by exercise is manifested by a decrease in muscle activation (28). In this regard, the decreased muscular force in those muscles not involved in the exercise reveals supraspinal fatigue (13,23). Other authors (18,21), to check whether supraspinal fatigue occurs after prolonged exercise, noted the absence of changes in the force of muscles not involved in a prolonged running exercise through measuring handgrip, which leads the authors to conclude that selective supraspinal fatigue does not occur in this type of exercise. Supporting this line of thought, Millet and Lepers (23) hypothesized that grip strength loss (muscles not involved) after running would be a good revealer of supraspinal fatigue.
Based on previous studies (4,5) and for a better understanding of the results obtained, the researchers decided to incorporate cluster analysis because members of the same cluster are likely to have more similar responses. Two clusters of endurance athletes were obtained from the different magnitude of the CMJ. As mentioned above, these clusters were categorized as responders (RG, n = 17; CMJ = +13.89%) and nonresponders (NRG, n = 13; remained unchanged). From this analysis, RG confirmed an improvement of CMJ in fatigued condition by enhancement of Ppower and Pforce. Interestingly, this group also experienced a significant increase in handgrip strength (+1.55 ± 4.37 kg, p = 0.05) and T400m performance (−1.68 ± 4.18 seconds, p = 0.049). In contrast, for NRG, a significant reduction in handgrip strength was shown (−1.05 ± 1.89 kg, p = 0.042) along with no significant trend to impair T400m performance (+1.69 ± 4.14 seconds). In relation to the mechanical parameters of CMJ, the main difference is in ConcW, which was seen to affect fatigue in contrary ways across both groups (+4.66% RG and −4.6% NRG), thereby reinforcing the conclusion obtained by other authors (4,5) of the negative influence of local fatigue on the capability of athletes to demonstrate PAP during power performance. All this could indicate that performance in endurance exercise routines is largely conditioned by the muscular adaptations that allow an optimal application and maintenance of force. This is a controversial topic and previous studies (4,5) suggest that participants suffer a smaller loss of Pforce during CMJ and therefore could maintain the overall mean power and improve the subsequent Ppower. The results obtained in this study support this rationale and that the RG is less affected by fatigue as the NRG in all mechanical parameters of CMJ analyzed (Ppower, Pforce, ConcW, and EccW), although only a significant difference (p = 0.004) between both RG and NRG in ConcW was shown.
Concerning handgrip strength, Racinais et al. (27) provided similar data to this study's findings in respect to an improvement in fatigued condition, but the authors concluded that grip strength does not change significantly during continuous or intermittent exercise. In this study, the impairment in handgrip performance in NRG and its significant increase in RG may be because of an increase in neural activity in this type of intermittent exercise. It has been suggested that the central nervous system is capable of partial recovery within a few seconds in this type of exercise (1,13). This is contrary to the contributions of previous studies (21,23) indicating that there is a reduction of maximum voluntary activation in prolonged efforts. As indicated in the study by Martin et al. (21), the ability of the central nervous system to activate muscles to the maximum may be altered only in continuous exercise. Millet and Lepers (23) also found no changes in handgrip strength after a 30-km race, concluding that this measure cannot lead to the conclusion that there is no selective supraspinal fatigue. However, in line with some authors (1,13,27), the researchers find it convenient to make this simpler type of measure to explore the possible existence of supraspinal fatigue after endurance exercise and its relationship to the phenomenon of potentiation.
As indicated in the study by Skof and Strojnik (32), PAP is possible despite high concentrations of lactate, and this study confirms that rationale, obtaining RG values very high in blood lactate (13.12 ± 2.55 mmol·kg−1·min−1). The significant correlation in RG between ΔLactate with ΔCMJ (r = 0.535, p = 0.027) is also worth noting. These results support those obtained by other researchers (17) who observed that subjects who tend to jump higher were those who were able to accumulate more lactate. Vuorimaa et al. (33) obtained different results for this correlation according to the run protocol carried out: a positive correlation in the intermittent running protocol (100% velocity associated with
) (r = 0.62, p < 0.01) and a lack of correlation in continuous running (80% velocity associated with
), although the improvements in CMJ are significant (p < 0.001) before and after both exercises. Similar findings where CMJ increases or remains at the same level have been researched during the early stages of an intermittent anaerobic test where blood lactate levels increased significantly above resting levels but not to maximum levels (10,17,22). Therefore, in intermittent running, the increase in the intensity of the exercise and blood lactate concentration seems to be associated with greater explosive force in long-distance runners.
However, for NRG, the correlation of ΔLactate with ΔCMJ was negative (r = −0.782, p = 0.002), which is to say the opposite of those results obtained for RG. This fact could be one of the reasons that some athletes experienced PAP and others did not, despite doing the same training protocol and having similar level of training, experience, and other characteristics. Numerous contrasting views exist regarding the physiological effects of lactate and its roles postproduction. There is, however, a clear association between the production of lactate and muscular fatigue (11). Muscle is now considered a consumer of lactate (11,14). The rate at which lactate is used is dependent on the rate of metabolism, blood flow, lactate concentration, hydrogen ion concentration, fiber type, and exercise training (14), which leads the researchers to believe that more research is needed to check whether some of these parameters could explain the presence or absence of PAP in athletes of similar level.
In conclusion, the effect of induced fatigue for an EIT protocol on vertical jump performance and handgrip strength shows that trained subjects can maintain their strength and power levels and, therefore, their work capacity. However, all athletes did not respond in the same manner to the exercise performed and this suggests that improvements in long-distance runners' performance after a training period may be due not only to metabolic adaptations but also to specific neuromuscular adaptations. Furthermore, the evaluation of power at the same time as running performance should be considered for the monitoring of endurance athletes.
From a practical point of view, the PAP responses are different in each subject and it would be advisable for these tasks to be individualized, with a rest period for each subject as suggested by different authors (2,8). In this sense, more longitudinal research is needed, which would control the presence of PAP at different moments in a training season. These studies should focus on the PAP phenomenon as an instrument used to control neuromuscular adaptations during resistance training. Postactivation potentiation obtained in this study has a mechanical explanation, but neither the molecular basis nor neuromuscular parameters were directly explored, so additional studies may need to address these issues.
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