Introduction: This study aimed to model the dissociation in the V˙O₂/power output (PO) relationship between ramp incremental (RI) and constant work rate (CWR) exercise and to develop a novel strategy that resolves this gap and enables an accurate translation of the RI V˙O₂ response into a constant PO. Methods: Nine young men completed two RI tests (30 and 15 W·min⁻¹) and CWR tests at seven intensities across exercise intensity domains. The V˙O₂/PO relationship for RI and CWR exercise was modeled, and the dissociation was compared in terms of PO. The accuracy of three translation strategies was tested in the moderate-intensity (i.e., zone 1) and heavy-intensity (i.e., zone 2) domain. Strategy 1 comprised a simple mean response time correction, whereas strategies 2 and 3 accounted for the loss of mechanical efficiency in zone 2 by applying an extra correction that was based on, respectively, the difference between s _{2 − CWR} and s _{2 − ramp} and the ratio s ₂/s ₁. Results: For all intensities, differences in PO were found between CWR and RI exercise (P < 0.001). Overall, these differences were smaller for the 15-W·min⁻¹ compared with the 30-W·min⁻¹ protocol (P = 0.012). Strategy 1 was accurate for PO selection in zone 1 (bias = 0.4 ± 7.3 W), but not in zone 2 (bias = 17.1 ± 15.9 W). Only strategy 2 was found to be accurate for both intensity zones (bias = 2.2 ± 14.2 W) (P = 0.107). Conclusion: This study confirmed that a simple mean response time correction works for PO selection in the moderate-intensity but not in the heavy-intensity domain. A novel strategy was tested and validated to accurately prescribe a constant PO based on the RI V˙O₂ response in a population of young healthy men.

Anthropometric evaluation of athletes is necessary to optimize talent identification and player development. Objectives: To provide a specific anthropometric reference database of senior male rugby players competing at different levels in the southern European region. Design: Cross-sectional. Methods: In 362 professional players (25 ± 4 y; 138 Italian national team, 97 first-division, and 127 second-division national championships) the authors measured mass, stature, and percentage body fat (plicometry). Mean, SD, and coefficient of variation were calculated for forwards and backs and for positional subgroups. Binomial logistic regression and receiver-operating-characteristic curve were performed to assess which variables best predicted level assignment (international vs national level). Results: For all competitive levels forwards were significantly heavier and taller and had a larger percentage body fat and fat-free mass than backs. The lower the competitive level, the higher the within-role variability observed; furthermore, players in a specific positional subgroup were lighter, shorter, and fatter and had less fat-free mass. Fat-free mass is the variable that best predicts the likelihood of being classified as an international or national player (cutoff value 79.54 kg). Conclusions: The data confirm the specificity in the physical requirements of rugby in individual playing positions at all competitive levels and document significant differences among elite and 1st- and 2nd-division players in the same positional role. These differences may reflect the variable technical abilities, selection, training practices, and requirements of the game among these categories.

Heart rate (HR) targets are commonly used to administer exercise intensity in sport and clinical practice. However, as exercise protracts, a time-dependent dissociation between HR and metabolism can lead to a misprescription of the intensity ingredient of the exercise dose. Purpose: We tested the hypothesis that a slow component of HR (i.e., scHR) occurs in all intensity domains, greater than the slow component of oxygen uptake (scV˙O₂), and we developed an equation to predict it across exercise intensities. Method: Eighteen healthy, postmenopausal women (54 ± 4 yr) performed on a cycle ergometer: i) a ramp incremental test for thresholds and V˙O_2max detection; ii) 30-min constant work exercise at 40%, 50%, 60%, 70%, and 80% V˙O_2max for the measurement of scHR, scV˙O₂, stroke volume, and body temperature (T°). scHR and scV˙O₂ were compared by two-way repeated-measures ANOVA (intensity and variable). Pearson correlation was calculated between the slow component of all variables, relative intensity, and domain. scHR (in beats per minute) was predicted with a linear model based on exercise intensity relative to the respiratory compensation point (RCP). Results: A positive scHR was present in all domains, twice the size of scV̇O₂ (P < 0.001), and significantly correlated with the slow components of V̇O₂ (r ² = 0.46), T° (r ² = 0.52), and relative intensity (r ² = 0.66). A linear equation accurately predicts scHR based on %RCP (r ² = 0.66, SEE = 0.15). Conclusions: A mismatch exists between the slow components of HR and metabolic intensity. Whenever exercise is prescribed based on HR, target values should be adjusted over time to grant that the desired metabolic stimulus is maintained throughout the exercise session.

Ferrari, L, Colosio, AL, Teso, M, and Pogliaghi, S. Performance and anthropometrics of classic powerlifters: Which characteristics matter? J Strength Cond Res 36(4): 1003–1010, 2022—The purpose of this study is: (a) provide normative performance and anthropometric data of Southern European classic powerlifters of both sexes; (b) determine the possible relationships between these variables and performance; and (c) develop population-specific predictive equations for single lifts and overall powerlifting performance. During an unofficial national-level competition, we recruited 74 athletes (51 men and 23 women) and recorded their individual, anthropometric, and performance characteristics and divided them into sex and 2 performance categories based on their Wilks points. Weaker (370 Wilks points) athletes of both sexes were compared by two-way analysis of variance. Simple correlation and multiple linear regression between individual/anthropometric characteristics and performance were modeled. We applied a step-forward multiple linear regression model to predict single lifts and overall performance. All parameters significantly differed between sexes (p < 0.05 for all comparisons). Stronger male athletes had a significantly larger neck (42 ± 2.8 cm; effect size [ES] = 0.59), and flexed (40.6 ± 3.3 cm; ES = 1.18) and relaxed upper-arm (37.5 ± 3.1 cm; ES = 1.34) and thigh girths (63.6 ± 7.0 cm; ES = 0.77) compared to weaker male athletes. Furthermore, stronger women had significantly larger flexed (32.6 ± 3.3 cm; ES = 0.88) and relaxed upper-arm (33 ± 1.5 cm; ES = 2.28) and chest girths (99.3 ± 9.2 cm; ES = 1.10) compared to weaker female athletes. A combination of experience, fat mass, and upper-limb and lower-limb muscle mass indexes can accurately and precisely predict overall and individual lift performance (r ² ≥ 0.83 for all the predictions). This is the first study to provide normative performance and anthropometric data in Southern European male and female powerlifters.