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.

Objectives: Determine if (a) a better trunk stability and endurance are associated with an improved whole‐body dynamic balance, and if (b) the assessment tests can be interchanged within each capability.Methods: Sixty‐three physically active young males performed three trunk stability (i.e., the lumbopelvic stability, the unstable sitting and the sudden loading sitting tests), three trunk muscle endurance (i.e., the Biering–Sørensen, the side bridge and the front bridge tests) and four whole‐body dynamic balance (i.e., the tandem and the single‐leg stance, the Y‐Balance, and the single‐leg triple hop tests) tests two times. After assessing the reliability of the variables, a Pearson correlation analysis was performed.Results: The correlations between trunk stability and endurance tests with dynamic balance tests were non‐significant except for the unstable sitting test with both the tandem (r = 0.502) and the single‐leg stance (r = 0.522) tests. Moreover, no relationships were observed between the trunk stability and the trunk muscle endurance tests. Interestingly, no relationships were found between most tests within each capability (i.e., trunk stability, trunk endurance, and dynamic balance) except: (i) the front bridge stability test and the back (r = 0.461) and the side (r = 0.499) bridge stability tests; (ii) the two side bridge endurance tests (r = 0.786); (iii) the tandem and the single‐leg stance tests (0.439 ≤ r ≤ 0.463); (iv) the Y‐Balance and the single‐leg triple hop tests (0.446 ≤ r ≤ 0.477).Conclusion: Better trunk function does not seem to be a relevant factor for dynamic balance in young active males. In this population, specific measures are needed as the test interchangeability is questioned.

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: 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.

Exercise‐induced hypoalgesia (EIH) is characterized as the pain reduction after an exercise session and it seems to be related to the release of plasma β‐endorphin. In this sense, the core stabilization training (CT) has been suggested for patients with chronic nonspecific low back pain (CNSLBP), but it is unclear whether it induces EIH. Patients with CNSLBP have neuromotor dysfunctions that can affect the performance of functional tasks, thus, performing functional training (FT) could improve motor control and promote EIH, since functional training uses multi‐joint exercises that aim to improve the functionality of actions performed in daily life. EIH is usually assessed using quantitative sensory tests (QST) such as conditioned pain modulation, pressure pain threshold, and temporal summation. Thus, the sum of parameters from quantitative sensory tests and plasma β‐endorphin would make it possible to understand what the neuroendocrine effects of FT and CT session are. Our study compared the acute effect of CT and FT on the EIH and plasma β‐endorphin release, and correlated plasma β‐endorphin with quantitative sensory testing in patients with CNSLBP. Eighteen women performed two training sessions (CT and FT) with an interval of 48 h between sessions. EIH was assessed by QST and plasma β‐endorphin levels. Results showed that only FT significantly increased plasma β‐endorphin (FT p < 0.01; CT p = 0.45), which correlated with pain pressure threshold (PPT) and conditioned pain modulation (CPM). However, QST values were not different in women with CNSLBP after CT or FT protocols. Plasma β‐endorphin correlated with PPT and CPM, however, the same did not occur with a temporal summation.

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.