Alberto Bottari

Background: Powerlifting is a discipline in which athletes aim to lift the maximum weight in 3 exercises: Squat, Bench Press, and Deadlift. Since the introduction of “Classic” powerlifting by the International Powerlifting Federation (IPF) in 2012, there has been an increase in popularity, athlete participation, and attention from sports science research. Previous studies have examined factors influencing the long-term longitudinal adaptation of upper- and lower-body strength, but no one used this information to develop predictive models of future classic powerlifting performances, especially considering the different age, sex, and weight categories, with the final aim of tailoring the medium- and long-term training goals. This study aims to develop and validate a machine learning-based linear regression model to predict single-lift and overall performance in classic powerlifters. The model considered variables such as sex, age, weight, initial strength levels, and competition history. The study also seeks to provide European normative powerlifting performance data across different categories to assist in talent identification and optimization of training. Results: The final dataset included 54,064 observations from 8,907 unique lifters. Normative data differed between sex, age categories, and initial strength level (p < 0.001). The predictive model demonstrated high predictive accuracy (Root mean Square of Error 10.41 to 19.4; R² 0.90 to 0.94), with no differences between mean values (p 0.733 to 0.930), extremely large correlations (r 0.95 to 0.97), and no significant bias (z-score − 1.78 to − 0.64) between predicted and actual performance values across all lifts. Conclusions: The developed machine learning model provides valid and accurate predictions of individual powerlifting performance, by accounting for various individual characteristics. The model can assist coaches and athletes in setting realistic training goals and monitoring progress. Moreover, normative data for each lift and total performance were provided, stratified by sex, age, weight category, and initial strength levels, offering valuable benchmarks for athletes and coaches.

Objectives: The aim of this study was to assess the safety and feasibility of resistance training (RT) in middle-aged and young individuals by examining cardiocirculatory and metabolic responses to squat performed under low and high external loads as per current exercise prescription guidelines. Methods: Eighteen RT-trained individuals (nine middle-aged individuals, including eight women who were equally distributed) performed a cycling incremental test for the determination of their maximal aerobic capacity and three sessions of RT, respectively, to determine their one repetition maximum (1RM) of squat and their physiological responses during different training protocols of squat with equal training loads (3 × 12 at 55% 1RM vs. 5 × 5 at 80% 1RM). Whole-body metabolic (oxygen update and blood lactate) and cardiocirculatory (heart rate and blood pressure) responses and rate of perceived exertion (RPE) were compared across age groups and % 1RM to determine the metabolic stimulus and cardiovascular strain imposed by this form of training. Results: Young and middle-aged individuals exhibited similar cardiocirculatory responses to RT, with the only exception being a higher diastolic response in the middle-aged group for both protocols (present also at rest). No difference was found between the two age groups in terms of metabolic response and RPE. 80% 1RM induced a similar cardiocirculatory response and a higher RPE but a lower metabolic response compared to 55% 1RM. Conclusions: While no difference in physiological responses was found between the groups, the lower-load and higher-repetition training scheme demonstrated better time efficiency, metabolic activation, and perceived effort with equivalent cardiocirculatory strain. These findings support the safety of RT and can guide practitioners in the design of training protocols.

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.

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.

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.