This study examined the reliability and validity of three methods of estimating the one-repetition maximum (1RM) during the free-weight prone bench pull exercise. Twenty-six men (22 rowers and four weightlifters) performed an incremental loading test until reaching their 1RM, followed by a set of repetitions-to-failure. Eighteen participants were re-tested to conduct the reliability analysis. The 1RM was estimated through the lifts-to-failure equations proposed by Lombardi and O’Connor, general load-velocity (L-V) relationships proposed by Sánchez-Medina and Loturco and the individual L-V relationships modelled using four (multiple-point method) or only two loads (two-point method). The direct method provided the highest reliability (coefficient of variation [CV] = 2.45% and intraclass correlation coefficient [ICC] = 0.97), followed by the Lombardi’s equation (CV = 3.44% and ICC = 0.94), and no meaningful differences were observed between the remaining methods (CV range = 4.95–6.89% and ICC range = 0.81–0.91). The lifts-to-failure equations overestimated the 1RM (3.43–4.08%), the general L-V relationship proposed by Sánchez-Medina underestimated the 1RM (−3.77%), and no significant differences were observed for the remaining prediction methods (−0.40–0.86%). The individual L-V relationship could be recommended as the most accurate method for predicting the 1RM during the free-weight prone bench pull exercise.

This aims of this study were (I) to determine the velocity variable and regression model which best fit the load-velocity relationship during the free-weight prone bench pull exercise, (II) to compare the reliability of the velocity attained at each percentage of the one-repetition maximum (1RM) between different velocity variables and regression models, and (III) to compare the within- and between-subject variability of the velocity attained at each %1RM. Eighteen men (14 rowers and four weightlifters) performed an incremental test during the free-weight prone bench pull exercise in two different sessions. General and individual load-velocity relationships were modelled through three velocity variables (mean velocity [MV], mean propulsive velocity [MPV] and peak velocity [PV]) and two regression models (linear and second-order polynomial). The main findings revealed that (I) the general (Pearson's correlation coefficient [r] range = 0.964–0.973) and individual (median r = 0.986 for MV, 0.989 for MPV, and 0.984 for PV) load-velocity relationships were highly linear, (II) the reliability of the velocity attained at each %1RM did not meaningfully differ between the velocity variables (coefficient of variation [CV] range = 2.55–7.61% for MV, 2.84–7.72% for MPV and 3.50–6.03% for PV) neither between the regression models (CV range = 2.55–7.72% and 2.73–5.25% for the linear and polynomial regressions, respectively), and (III) the within-subject variability of the velocity attained at each %1RM was lower than the between-subject variability for the light-moderate loads. No meaningful differences between the within- and between-subject CVs were observed for the MV of the 1RM trial (6.02% vs. 6.60%; CV_ratio = 1.10), while the within-subject CV was lower for PV (6.36% vs. 7.56%; CV_ratio = 1.19). These results suggest that the individual load-MV relationship should be determined with a linear regression model to obtain the most accurate prescription of the relative load during the free-weight prone bench pull exercise.

The objectives of this revision were to identify game indicators, the references for evaluating tactical efficiency, and the types of data analysis used in invasion team sports for the analysis of tactical performance. A search of the principal electronic databases was carried out, the procedure was adjusted to the PRISMA-P 2015 protocol, and 62 non-experimental published articles were obtained from the years 2000–2016. The results showed the complimentary use of diverse types of indicators and the frequent elaboration of instruments ad hoc. The individual technical-tactical actions were widely used. The record of standardised team actions was scarce. The research lines with a sequential data record for their temporary analysis were significant. The transitory or final score and the results of game sequences were the most used references of performance. The big data tools of analysis, web analysis, and the detection of temporal patterns or the application of polar coordinates were emerging methods of analysis.

Background: The aim of this study was to examine whether the addition of very light loads for modeling the force–velocity (F–V) relationship during the bench press (BP) exercise can confirm its experimental linearity as well as to increase the reliability and concurrent validity of the F–V relationship parameters (maximum force (F₀), maximum velocity (V₀), F–V slope, and maximum power (P_max)). Method: The F–V relationship of 19 healthy men were determined using three different methods: (I) 6-loads free method: six loads performed during the traditional free-weight BP exercise (≈ 1–8–29–39–49–59 kg), (II) 4-loads free method: four loads performed during the traditional free-weight BP exercise (≈ 29–39–49–59 kg), and (III) 4-loads Smith method: four loads performed during the ballistic bench press throw exercise in a Smith machine (≈ 29–39–49–59 kg). Results: The linearity of the F–V relationship was very high and comparable for the three F–V methods (p = 0.204; median Pearson’s correlation coefficient (r) = 0.99). The three methods were ranked from the most to the least reliable as follows: 6-loads free (coefficient of variation (CV) range = 3.6–6.7%) > 4-loads Smith (CV range = 4.6–12.4%) > 4-loads free (CV range = 3.8–14.5%). The higher reliability of the 6-loads free method was especially pronounced for F–V slope (CV_ratio ≥ 1.85) and V₀ (CV_ratio ≥ 1.49) parameters, while the lowest difference in reliability was observed for F₀ (CV_ratio ≤ 1.27). The 6-loads free and 4-loads free methods showed a very high concurrent validity respect to the 4-loads Smith method for F₀ and P_max (r ≥ 0.89), a moderate validity for the F–V slope (r = 0.66–0.82), and a low validity for V₀ (r ≤ 0.37). Discussion: The routine testing of the F–V relationship of upper-body muscles through the BP exercise should include trials with very light loading conditions to enhance the reliability of the F–V relationship.

Objective: This systematic review and meta-analysis aimed to (I) evaluate the evidence on the effects of high-intensity interval training (HIIT) on systolic blood pressure (SBP) and diastolic blood pressure (DBP) in hypertensive patients; (II) determine whether HIIT impacts SBP and DBP differently; and (III) assess the clinical relevance of these effects. Methods: A comprehensive search was conducted across multiple electronic databases, resulting in the inclusion of seven randomized clinical trials in the meta-analysis. The outcomes were analyzed using random-effects models to compute mean differences (MD) and standardized mean differences (SMD) for SBP and DBP. Results: A small reduction in SBP was observed with HIIT interventions (MD −3.00; 95% CI −4.61 to −1.39; p < 0.0001; SMD −0.28; 95% CI −0.42 to −0.13; p = 0.0003). However, no statistically significant reductions were detected for DBP (MD −0.70; 95% CI −1.80 to 0.39; p = 0.21; SMD −0.07; 95% CI −0.22 to 0.08; p = 0.35). Despite demonstrating statistical significance for SBP, the effects did not reach clinical relevance. Conclusions: HIIT interventions yield small reductions in SBP, with minimal impact on DBP. These findings suggest limited clinical relevance in the management of hypertension. Further randomized controlled trials are necessary to standardize HIIT protocols, with specific emphasis on intensity control and manipulation, to better understand their potential role in hypertensive populations.

Background: This study aimed to explore whether a predictive model based on body composition and physical condition could estimate seasonal playing time in professional soccer players.Methods: 24 professional soccer players with 5–7 years of professional experience participated. Body composition and physical condition variables were assessed, and total minutes played during the season were recorded as the dependent variable. Correlations between variables were examined to reduce multicollinearity, followed by a principal component analysis (PCA) of the selected predictors. The first three components were used as inputs in a Gradient Boosting model. Model performance was evaluated using 5-fold cross-validation and leave-one-out cross-validation (LOOCV).Results: High intercorrelations among independent variables (r > 0.70) justified dimensionality reduction through PCA. The first three components explained 70% of the total variance. However, no direct correlations were observed between individual variables and minutes played, and the Gradient Boosting model did not achieve positive predictive performance under cross-validation (5-fold CV: R² = −0.04; LOOCV: R² < 0).Conclusion: In this small dataset, a multivariate approach combining PCA and Gradient Boosting did not yield predictive accuracy for playing time. Nonetheless, the PCA revealed meaningful structures in the players’ physical and body composition profiles, which may inform future research. Larger and more heterogeneous samples are required to determine whether component-based predictors can reliably estimate playing time in professional soccer.