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.

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.

Purpose: This study analyzed the acute mechanical response to three workouts of the day (WOD) protocols in as many repetitions as possible (AMRAP), every minute on the minute (EMOM), and for time (FT) models by quantifying the degree of mechanical fatigue induced by popular resistance exercises in the Cross modalities, front squat (FS), and shoulder press (SP). We also analyzed whether the exercises’ fastest velocity (Vfastest) could be an objective indicator of relative intensity (%1RM). Methods: Nine trained men performed three FS and SP exercises protocols. The degree of fatigue was quantified by the velocity loss (VL) achieved in both exercises and the velocity loss achieved in the WOD (VL_WOD). Results: The VL_WOD in the AMRAP, EMOM, and FT protocols was 73.2 ± 10.9%, 61.6 ± 15.1%, and 76.1 ± 8.8%, respectively. In the AMRAP and FT protocol, the V_fastest showed very strong relationships with the %1RM for FS and SP (r = −0.83, −0.75, respectively, p < .01); while in the EMOM protocol, there was a strong relationship between these variables, only for the SP (r = −0.61, p < .05). In the FT protocol, we observed an extremely strong relationship for FS (r = −0.91, p < .001) and very strong (r = −0.71, p < .05) for SP between these variables. Conclusion: Therefore, the AMRAP and FT training models induce the highest degrees of mechanical fatigue in the FS and SP exercises, and the V_fastest is a reliable tool for estimating relative intensity in resistance exercises of Cross modalities.

The objective of this study was to monitor the training loads (TL) and well-being of elite rhythmic gymnastics (RG) athletes, as well as compare these variables between starters and reserve gymnasts during 25 weeks of training. Ten athletes from the Brazilian national RG team (17.4 ± 1.1 y of age) were monitored during the general preparatory period (GPP), specific preparatory period (SPP), and pre-competitive period (PCP). The internal TL was quantified with the use of sessional ratings of perceived exertion (sRPE). We assessed well-being daily with a well-being scale. The TL, duration, monotony, and strain were calculated weekly. We found that the internal TL and session durations were 9242 ± 2511 AU and 2014 ± 450 min, respectively. The internal TL, strain, and monotony were greater in the PCP than in the GPP and SPP for starters. In the SPP, there were statistical differences in internal TL (p = 0.036) and strain (p = 0.027) between starters and reserves. In the PCP, there were also statistical differences between starters vs. reserves athletes regarding internal TL (p = 0.027) and strain (p = 0.05). There was no statistically significant difference in well-being between the periods assessed. In conclusion, RG athletes display a higher TL magnitude during the PCP, whereas only reporting non-significant minor variations in well-being. In addition, there is a discrepancy in the TL between starters and reserves.

Nos estudos iniciais, que documentaram os efeitos positivos do treinamento com pesos e a execução de esforços musculares repetidos, o propósito da ciência de conhecer a melhor maneira de definir, controlar e dosar o treinamento de força tem sido uma das questões que concentraram o maior interesse e esforço. Trata-se de uma questão extremamente importante, pois os resultados originários dos trabalhos científicos de maior qualidade devem possibilitar a continuação da geração do corpo de conhecimento que ajuda a melhorar a metodologia do treinamento e, portanto, as participações na prática dos profissionais. Para que isso seja cumprido, os estudos científicos devem ter, entre outros atributos, um método preciso de determinação e controle das variáveis que definem o estímulo do treinamento proposto, a fim de verificar a relação entre ele e os efeitos produzidos. No entanto, se isso não acontecer, pesquisadores e profissionais do treinamento correm o risco de tomar decisões sobre a configuração dos estímulos (manipulação das variáveis da carga) com base em conclusões científicas "falsas" ou incertas, na melhor das hipóteses.

Purpose: This study aimed to profile in-season training load management across different age categories in elite Brazilian male youth footballers, and to compare differences between playing positions (external defenders, central defenders, central midfielders, external midfielders, and forwards) and load structure relative to match-day. Methods: During the competitive season, data were collected from 43 players (175.8 ± 6.3 cm, 69.8 ± 8.1 kg) across three age groups (U15: n = 14; U17: n = 16; U20: n = 13), all from the same professional club. Positional data were collected using GPS devices during training sessions. A linear mixed model was used to compare age groups, training days, and positions, with ‘Athlete ID’ included as a random effect. Statistical significance was set at p < 0.05. Results: U15 and U20 players covered significantly greater total distances than U17 players (p = 0.01; t = 6.52, p < 0.01; t = 5.92, respectively), while U20s performed more decelerations than U15s (p < 0.02; t = 2.81). Total distances were higher on MD–4 and MD–3, and lowest on MD–1 and MD+2 (p < 0.05). High-speed running, sprinting, accelerations, and decelerations were greater on MD–4 and MD–2 across all age groups (p < 0.05). Conclusions: Training load variations across age groups largely reflect coaches’ activity choices, reflecting strategies to promote development while minimising pre-match fatigue. Concerning position-specific differences, external defenders demonstrated the greatest exposure to high-intensity actions and sprinting during the microcycles, regardless of the age category (p < 0.05). This greater external load is likely associated with position-specific demands, particularly overlapping runs and recovery sprints during both training sessions and matches.