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.

A new technology (BlazePod™) that measures response time (RT) is currently on the market and has been used by strength and conditioning professionals. Nevertheless, to trust in the measurement, before the use of a new device to measure any outcome in the research or clinical setting, a reliability analysis of its measurement must be established (Koo and Li, 2016). Hence, we assessed the test-retest reliability (repeatability) of the BlazePod™ (Play Coyotta Ltd., Aviv, Israel) technology during a pre-defined activity to provide information about the level of agreement and the magnitude of errors incurred when using the technology. This information can assist practitioners and researchers in the use of BlazePod™ technology. We recruited 24 physically active young adults (age = 23.9 ± 4.0 years; height = 1.67 ± 0.09 m; body mass = 68.2 ± 13.1 kg), who were free of injuries, and any orthopedic, or cardiorespiratory diseases. Participants reported to the laboratory on two occasions, separated by one week. One week before, participants performed a familiarization session with the instrument. During the first session, the one-leg balance activity (OLBA) was performed. This activity was chosen randomly among all BlazePod™ pre-defined activities. We conducted all sessions in a physiology laboratory at the same time for each participant and under similar environmental conditions (~23° C; ~60% humidity). The OLBA consisted of a unipedal balance activity performed with four pods arranged in a square on the floor. Participants stood up in the center of the square, and the OLBA aim was to tap out as many lights as possible with the dominant foot during 30 seconds. The system lighted up in a random order not known by the participants neither the researchers. The distance between the Pods was the individual lower limb length. Three trials were performed. The best value obtained was recorded. A one-minute rest interval between all trials was given. The total number of taps and average RT of all taps in the OLBA were recorded for further analysis. Data are presented as mean ± SD or 95% confidence interval (CI). We confirmed the normal data distribution using the Shapiro-Wilk test. A paired t-test, Cohen’s d effect size (ES) and its 95% CI were calculated to assess the magnitude of the mean difference between sessions. The interpretation of the ES was: trivial (<0.20), small (0.20-0.59), moderate (0.60-1.19), large (1.2-2.0) and very large (>2.0) effect (Hopkins et al., 2009). The intraclass correlation coefficient (ICC) and its 95% CI was used to assess the reliability based on a single measurement, absolute-agreement, two-way mixed-effects model. The ICC value was interpreted as follows: poor (<0.5), moderate (0.5-0.75), good (0.75-0.9), and excellent (>0.9) reliability (Koo and Li, 2016). We also calculated the standard error of measurement (SEM), the coefficient of variation (CV), the smallest detectable change (SDC), the level of agreement between sessions by a Bland-Altman plot, the systematic bias, and its 95% limits of agreement (LoA = bias ± 1.96 SD) (Bland and Altman, 1986). We observed a small to moderate increase between sessions for the number of taps (Day 1 = 20 ± 3 taps, Day 2 = 22 ± 4 taps; t(23) = -4.121; p < 0.001; ES = 0.55, 95% CI = 0.43 to 0.67) and a trivial to small decrease for the RT (Day 1 = 1418 ± 193 ms, Day 2 = 1358 ± 248 ms; t(23) = 1.721; p = 0.099; ES = -0.27, 95% CI = -0.15 to -0.38 CI). All reliability indexes for both outcome measures are shown in Table 1. Moderate to excellent levels of reliability were found by the ICC (95% CI) values and acceptable reliability by the CV for both measures. Bland-Altman plots are depicted in Figure 1. The systematic bias that we found showed that on average in the second day, participants achieved two taps more than the first day and were 59 ms faster than the first day. The LoA showed that the number of taps measured in the first day might be 7 units below or 3 units above Day 2. Besides, the RT measured in Day 1 might be 272 ms below or 391 ms above Day 2. In conclusion, the BlazePod™ technology provides reliable information during its OLBA in physically active young adults. We considered the measurement error as acceptable for practical use since low systematic biases and errors of measurement were reported in this study, besides a moderate ICC and excellent CV. These results suggest that practitioners can use the information provided by the BlazePod™ technology to monitor performance changes during cognitive training and to evaluate the effects of a training intervention.

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Objectives: The purposes of this investigation were: 1) to compare the load-velocity relationship estimated by the two-point method between untrained men and women during the parallel back squat exercise (BS) and 2) to compare the load-velocity profile found in our study with the load-velocity profiles reported in the scientific literature for trained individuals. Beyond, we aimed to compare the measured 1RM velocity with predicted 1RM velocity by the two-point method in the BS exercise in untrained individuals. Methods: Seventy-six untrained individuals (38 men (22.7 ± 4.4 years; 174.9 ± 6.8 cm; 76.1 ± 14.9 kg) and 38 women (24.7 ± 4.3 years; 159.1 ± 6.0 cm; 64.7 ± 13.3 kg) performed a one-repetition maximum test and a progressive two-load test with 20% 1RM and 70% 1RM to estimate their load-velocity relationships. Results: The main results revealed that 1) mean propulsive velocity and mean velocity attained at each relative load were different between men and women (p < 0.05). However, the measured 1RM velocity was not significantly different between them. Untrained men provided a steeper load-velocity relationship than women. We found that 2) untrained individuals of our study showed a different load-velocity profile than trained individuals from scientific literature studies. Furthermore, 3) the measured 1RM velocity was lower than the predicted 1RM velocity (p < 0.05). Conclusion: These results suggest that the load-velocity relationship is dependent on sex and training background, and the two-point method using 20% and 70% 1RM might not be reliable to estimate the load-velocity relationship in the BS exercise for untrained men and women.