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.

Inertial Motor sensors (IMUs) are valid instruments for measuring postural sway but their ability to detect changes derived from visual deprivation in healthy older adults requires further investigations. We examined the validity and relationship of IMU sensor-derived postural sway measures compared to force plates for different eye conditions in healthy older adults (32 females, 33 males). We compared the relationship of the center of mass and center of pressure (CoM and CoP)-derived total length, root means square (RMS) distance, mean velocity, and 95% confidence interval ellipse area (95% CI ellipse area). In addition, we examined the relationship of the IMU sensor in discriminating between open- (EO) and closed-eye (EC) conditions compared to the force plate. A significant effect of the instruments and eye conditions was found for almost all the variables. Overall, EO and EC variables within (force plate r, from 0.38 to 0.78; IMU sensor r, from 0.36 to 0.69) as well as between (r from 0.50 to 0.88) instruments were moderately to strongly correlated. The EC:EO ratios of RMS distance and 95% CI ellipse area were not different between instruments, while there were significant differences between total length (p = 0.973) and mean velocity (p = 0.703). The ratios’ correlation coefficients between instruments ranged from moderate (r = 0.65) to strong (r = 0.87). The IMU sensor offers an affordable, valid alternative to a force plate for objective, postural sway assessment.

The 5-Sit-to-stand test (5STS) is widely used to estimate lower limb muscle power (MP). An Inertial Measurement Unit (IMU) could be used to obtain objective, accurate and automatic measures of lower limb MP. In 62 older adults (30 F, 66 ± 6 years) we compared (paired t-test, Pearson’s correlation coefficient, and Bland-Altman analysis) IMU-based estimates of total trial time (totT), mean concentric time (McT), velocity (McV), force (McF), and MP against laboratory equipment (Lab). While significantly different, Lab vs. IMU measures of totT (8.97 ± 2.44 vs. 8.86 ± 2.45 s, p = 0.003), McV (0.35 ± 0.09 vs. 0.27 ± 0.10 m∙s⁻¹, p < 0.001), McF (673.13 ± 146.43 vs. 653.41 ± 144.58 N, p < 0.001) and MP (233.00 ± 70.83 vs. 174.84 ± 71.16 W, p < 0.001) had a very large to extremely large correlation (r = 0.99, r = 0.93, and r = 0.97 r = 0.76 and r = 0.79, respectively, for totT, McT, McF, McV and MP). Bland–Altman analysis showed a small, significant bias and good precision for all the variables, but McT. A sensor-based 5STS evaluation appears to be a promising objective and digitalized measure of MP. This approach could offer a practical alternative to the gold standard methods used to measure MP.

The 5-Sit-to-stand test (5STS) is used for lower limb muscle power (MP) determination in field/clinical setting. From the time taken to perform five standing movements and three partially verified assumptions (vertical displacement, mean concentric time, and mean force), MP is estimated as the body’s vertical velocity x force. By comparison with a gold standard, laboratory approach (motion capture system and force plate), we aimed to: (1) verify the assumptions; (2) assess the accuracy of the field-estimated MP (MP_field); (3) develop and validate an optimized estimation (MP_field-opt). In 63 older adults (67 ± 6 years), we compared: (i) estimated and measured assumptions (2-WAY RM ANOVA), (ii) MP_field and MP_field-opt with the reference/laboratory method (MP_lab) (2-WAY RM ANOVA, Pearson’s correlation coefficient (r), Bland-Altman analysis). There was a significant difference between estimated and measured assumptions (p < 0.001). Following the implementation of the optimized assumptions, MP_field-opt (205.1 ± 55.3 W) was not significantly different from M_lab (199.5 ± 57.9 W), with a high correlation (r = 0.86, p < 0.001) and a non-significant bias (5.64 W, p = 0.537). Provided that corrected assumptions are used, 5STS field test is confirmed a valid time- and cost-effective field method for the monitoring of lower limbs MP, a valuable index of health status in aging.

Force–velocity profiling (F-v) evaluates muscle function through the identification of maximum force (F0), velocity (V0), power (Pmax) and optimal velocity (Vopt). The purpose of this study was to investigate the validity and feasibility of loaded 5 Sit-to-Stand (5STS) force-velocity profiling compared to the gold standard instruments (isokinetic Dynamometry, ISO) and asses the relationship between the 5STS-derived muscle function indexes with clinical markers of muscle mass and strength. Forty-six older adults (21 females: 63.8 ± 3.9 yrs) performed 5STS (four different weight conditions) and ISO tests (five different velocities). Paired t-tests, regression analyses, and Bland–Altman analysis were conducted. The results showed significant differences in F0, V0, and Vopt (p < 0.001) but no difference in Pmax (p = 0.259) between tests. Only F0 and Pmax were highly correlated between tests (r = 0.71, r = 0.84, respectively). Bland–Altman analysis showed a not significant bias and good precision (p = 0.260, 34 W) only for Pmax. Large to very large correlations (r = 0.53 to 0.81) were found between F0 and Pmax and clinical markers of muscle mass and strength. In conclusion, loaded 5STS profiling could be a feasible, valid, and cost- and time-efficient alternative to ISO for the characterization of clinically relevant markers of muscle function in healthy older adults.

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.