Background: Functional fitness training (FFT) is a new exercise modality that targets functional multi-joint actions via both muscle-strengthening exercises and aerobic training intervals. The aim of the study was to examine muscle recovery over a 20 min period after an FFT workout in trained adults. Materials and methods: Participants were 28 healthy trained subjects. In a single session, a countermovement jump (CMJ) was performed to determine several mechanical variables (jump height, maximum velocity, power) before (preFFT) and 4, 10, and 20 min after the FFT workout (postFFT). In parallel, capillary blood lactate concentrations were measured pre- and 3 min postFFT. Heart rate was also measured before and after the workout, and perceived exertion was measured postFFT. Results: Significant differences between the time points preFFT and 4 min and 10 min postFFT, respectively, were produced in jump height (p = 0.022, p = 0.034), maximum velocity (p = 0.016, p = 0.005), average power relative (p = 0.018, p = 0.049), and average power total (p = 0.025, p = 0.049). No differences were observed in any of the variables recorded preFFT and 20 min postFFT. Conclusions: While mechanical variables indicating muscle fatigue were reduced 4 and 10 min postFFT, pre-exercise jump ability only really started to recover 20 min after FFT although not reaching pre-exercise levels. This means that ideally intervals of around 20 min of rest should be implemented between training bouts.
Empirically, it is widely discussed in “Cross” modalities that the pacing strategy developed by an athlete or trainee has a significant impact on the endurance performance in a WOD in the AMRAP, EMOM, or FOR TIME model. We can observe at least six pacing strategies adopted during the cyclical modalities in the endurance performance in the scientific literature. However, besides these modalities, exercises of acyclical modalities of weightlifting and gymnastics are performed in the “Cross” modalities. These exercises may not allow the same pacing strategies adopted during cyclic modalities’ movements due to their motor characteristics and different intensity and level of effort imposed to perform the motor gesture. In addition to the intensity and level of effort that are generally unknown to the coach and athlete of the “Cross” modalities, another factor that can influence the adoption of a pacing strategy during a WOD in the AMRAP, EMOM, or FOR TIME model is the task endpoint knowledge, which varies according to the training model used. Thus, our objective was to evaluate situations in which these factors can influence the pacing strategies adopted in a self-regulated task with cyclic and acyclic modalities movements during an endurance workout in the AMRAP, EMOM, and FOR TIME model. Given the scarcity of studies in the scientific literature and the increasing discussion of this topic within the “Cross” modalities, this manuscript can help scientists and coaches better orient their research problems or training programs and analyze and interpret new findings more accurately.
Background: The aim of the study was to analyze the use of variables such as % of one-repetition maximum (1RM) and number of maximal repetitions (xRM) with execution velocity to define and control the intensity of resistance training in bench press exercise. Hence, exercise professionals will achieve better control of training through a greater understanding of its variables. Methods: In this cross-sectional study, fifty male physical education students were divided into four groups according to their relative strength ratio (RSR) and performed a 1RM bench press test (T1). In the second test, participants performed repetitions to exhaustion (T2), using a relative load corresponding to 70% 1RM determined through the mean propulsive velocity (MPV) obtained from the individual load–velocity relationship. This same test was repeated a week later (T3). Tests were monitored according to the MPV of each repetition and blood lactate values (LACT). Results: Regarding MPV, the best (fastest) repetition of the set (MPVrep Best) values were similar between groups (0.62 m·s−1–0.64 m·s−1), with significant differences in relation to the high RSR group (p < 0.001). The average maximum number of repetitions (MNR) was 12.38 ± 2.51, with no significant differences between the RSR groups. Nonetheless, significant variation existed between groups with regards to MNR (CV: 13–29%), with greater variability in the group corresponding to the lowest RSR values (CV: 29%). The loss of velocity in the MNR test in the different groups was similar (p > 0.05). Average LACT values (5.72 mmol·L−1) showed significant differences between the Medium RSR and Very Low RSR groups. No significant differences were found (p > 0.05) between T2 and T3 with regards to MNR, MPVrep Best, or MPVrep Last, with little variability seen between participants. Conclusions: The use of variables such as the 1RM, estimated using an absolute load value, or an MNR do not allow an adequate degree of precision to prescribe and control the relative intensity of resistance training. Besides, execution velocity control can offer an adequate alternative to guarantee an accurate prescription of intensity with regard to resistance training.
Currently, training models based on the maximum number of repetitions/rounds or on the execution of a proposed task in the shortest possible time have been gaining ground among Physical Education professionals. However, in our opinion, these models have significant drawbacks that oppose their use in the health context. Thus, we provide an analysis of the problems related to the control and magnitude of the training load (volume and intensity), distribution, duration, and characteristics of the recovery intervals and, of course, the intra-session density. This analysis was made without having measured each of these proposals directly. It is based on the reflection of the dynamics of the efforts made and potential fatigue generated. We hope to be able to verify and provide accurate and reliable data that may support and confirm the hypothesis generated through this analysis.
Background: the aim of this study was to analyse muscle fatigue and metabolic stress at 15 min of recovery after performing two independent sessions of functional fitness training (FFT): a session of strength functional fitness training (FFTstrength) and a session of endurance functional fitness training (FFTendurance). Methods: eighteen well-trained men conducted two protocols, separated by one week of rest: FFTstrength (3 sets of 21, 15 and 9 repetitions of Thruster with bar + Pull ups) and FFTendurance (3 sets × (30 kcal rowing + 15 kcal assault air bike)). Neuromuscular fatigue and metabolic stress were measured right before, right after and at 10 and 15 min after completing the FFT workout, as well as the mean heart rate (HRmean) and the rating of perceived exertion (RPE) at the end of the FFT. Results: FFTendurance recovered the velocity loss values after 15 min of recovery. On the other hand, FFTstrength only recovered velocity in the 1 m·s−1 Tests in squat (SQ), since the velocity levels were 7% lower in the 1 m·s−1 Tests in military press exercise (MP) after 15 min. Conclusions: These data indicate that there are specific recovery patterns not only as a function of the exercise and the body regions involved, but also regarding the recovery of neuromuscular and metabolic factors, since both FFT workouts obtained high blood lactate concentrations.
Background: The aim of this study was to verify the reproducibility of a resistance training protocol in the bench press (BP) exercise, based on traditional recommendations, analysing the effect of the muscle fatigue of each set and of the whole exercise protocol. Methods: In this cross-sectional study, thirty male physical education students were divided into three groups according to their relative strength ratio (RSR), and they performed a 1RM BP test (T1). In the second session (T2), which was one week after T1, the participants performed a BP exercise protocol of three sets with the maximum number of repetitions (MNR) possible to muscle failure, using a relative load corresponding to 70% 1RM determined through the mean propulsive velocity (MPV) obtained from the individual load–velocity relationship, with 2 min rests between sets. Two weeks later, a third session (T3) identical to the second session (T2) was performed. The MPV of each repetition of each set and the blood lactate level after each set were calculated, and mechanical fatigue was quantified through the velocity loss percentage of the set (% loss MPV) and in a pre-post exercise test with an individual load that could be lifted at ~1 m·s−1 of MPV. Results: The number of repetitions performed in each set was significantly different (MNR for the total group of participants: set 1 = 12.50 ± 2.19 repetitions, set 2 = 6.06 ± 1.98 repetitions and set 3 = 4.20 ± 1.99 repetitions), showing high variation coefficients in each of the sets and between groups according to RSR. There were significant differences also in MPVrep Best (set 1 = 0.62 ± 0.10 m·s−1, set 2 = 0.42 ± 0.07 m·s−1, set 3 = 0.36 ± 0.10 m·s−1), which significantly reduced the % loss MPV of all sets (set 1 = 77.4%, set 2 = 64%, set 3 = 54.2%). The lactate levels increased significantly (p < 0.05) (set 1 = 4.9 mmo·L−1, set 2 = 6 mmo·L−1, set 3 = 6.5 mmo·L−1), and MPV loss at 1 m·s−1 after performing the three sets was 36% in T2 and 34% in T3, with acceptable intrasubject variability (MPV at 1 m·s−1 pre-exercise: SEM ≤ 0.09 m·s−1, CV = 9.8%; MPV at 1 m·s−1 post-exercise: SEM ≤ 0.07 m·s−1, CV = 11.7%). Conclusions: These exercise propositions are difficult to reproduce and apply. Moreover, the number of repetitions performed in each set was significantly different, which makes it difficult to define and control the intensity of the exercise. Lastly, the fatigue generated in each set could have an individual response depending on the capacity of each subject to recover from the preceding maximum effort.
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