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) and stronger (>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 (r2 ≥ 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.

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.PurposeWe 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.MethodEighteen healthy, postmenopausal women (54 ± 4 yr) performed on a cycle ergometer: i) a ramp incremental test for thresholds and V˙O2max detection; ii) 30-min constant work exercise at 40%, 50%, 60%, 70%, and 80% V˙O2max 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).ResultsA positive scHR was present in all domains, twice the size of scV̇O2 (P < 0.001), and significantly correlated with the slow components of V̇O2 (r2 = 0.46), T° (r2 = 0.52), and relative intensity (r2 = 0.66). A linear equation accurately predicts scHR based on %RCP (r2 = 0.66, SEE = 0.15).ConclusionsA 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.

We tested the hypothesis that static stretching, an acute, non-metabolic fatiguing intervention, reduces exercise tolerance by increasing muscle activation and affecting muscle bioenergetics during cycling in the “severe” intensity domain. Ten active men (24±2 years, 74±11 kg, 176±8 cm) repeated an identical constant load cycling test, two tests were done in control conditions and two after stretching, that caused a 5% reduction of maximal isokinetic sprinting power output. We measured: i) oxygen consumption (VO2); ii) electromyography: iii) deoxyhemoglobin iv) blood lactate ([La-]); v) time to exhaustion (TTE) vi) perception of effort. Finally, VO2 and deoxyhemoglobin kinetics were determined. Force reduction following stretching was accompanied by augmented muscle excitation at a given workload (p=0.025), and a significant reduction in TTE (p=0.002). The time to peak of VO2 was reduced by stretching (p=0.034), suggesting an influence of the increased muscle excitation on the VO2 kinetics. Moreover, stretching was associated with a mismatch between O2 delivery and utilization during the on-kinetic, increased perception of effort and [La-], that are all compatible with an increased contribution of the glycolytic energy system to sustain the same absolute intensity. These results suggest a link between exercise intolerance and the decreased ability to produce force. Novelty bullets: • We provided the first characterization of the effects of prolonged stretching on the metabolic response during severe cycling. • Stretching reduced maximal force, augmented muscle activation in turn increasing the metabolic response to sustain exercise.

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Maximal Lactate steady-state (MLSS) demarcates sustainable from unsustainable exercise and is used for evaluation/monitoring of exercise capacity. Still, its determination is physically challenging and time-consuming. This investigation aimed at validating a simple, submaximal approach based on blood lactate accumulation ([Δlactate]) at the third minute of cycling in a large cohort of men and women of different ages. 68 healthy adults (40♂, 28♀, 43 ± 17 years (range 19-78), VO2max 45 ± 11 ml-1·kg-1·min-1 (25-68)) performed 3-5 constant power output (PO) trials with a target duration of 30 minutes to determine the PO corresponding to MLSS. During each trial, [Δlactate] was calculated as the difference between the third minute and baseline. A multiple linear regression was computed to estimate MLSS based on [Δlactate], subjects` gender, age and the trial PO. The estimated MLSS was compared to the measured value by paired t-test, correlation, and Bland-Altman analysis. The group mean value of estimated MLSS was 180 ± 51 W, not significantly different from (p = 0.98) and highly correlated with (R2 = 0.89) measured MLSS (180 ± 54 watts). The bias between values was 0.17 watts, and imprecision 18.2 watts. This simple, submaximal, time- and cost-efficient test accurately and precisely predicts MLSS across different samples of healthy individuals (adjusted R2 = 0.88) and offers a practical and valid alternative to the traditional MLSS determination.