Silvia Pogliaghi

Purpose The “traditional” load-velocity(L-V) relationship profiling and 1 R.M. estimation require multiple loads, which limits their applicability. We tested the feasibility and validity of an alternative "in situ" L-V profiling method using lifting velocities of simulated training sessions. Method We retrospectively analyzed a publicly available dataset on 51 resistance-trained individuals who performed: direct 1 R.M. measures, “traditional” L-V profiling test, and simulated training sessions during which “in situ” individual weight and velocity were recorded. Theoretical maximal load (L ₀ ), movement velocity (V ₀ ), regression slope (LV _slope ), goodness of fit (R ² ), and estimation of 1 R.M. were computed and compared between “traditional” and “in situ” methods. Both 1RM predictions were compared vs the directly measured 1RM. Results 1 R.M. and V _1RM mean values were 128 ± 37.6 kg and 0.35 ± 0.08 m•s ^− 1 , respectively. L ₀ , V ₀ , LV _slope , R ² , and 1 R.M. estimations were not different (p > 0.05) between L-V profiling methods. Moreover, both estimates of 1R.M. showed no significant difference (p > 0.05), extremely high correlation (r ≥ 0.97), and not significant bias ( _trad L-V, bias = 1.7 kg, precision = 9.7kg, p > 0.05; _{in situ} L-V, bias = 0.7 kg, precision = 7.6kg, p > 0.05; ) vs the directly measured 1R.M.. Conclusions In adult recreational lifters, the “in situ” L-V profiling is a feasible and valid method for the characterization of muscle function; it represents a safe and time-efficient alternative to “traditional” L-V profiling and direct 1 R.M. determination in the free weight barbell back squat exercise.

Alterations in motor learning are associated with Parkinson’s disease (PD). Possible body segment-specificity remains unexplored and could have practical implications for testing and rehabilitation. Our aim was to investigate sequence-specific implicit motor learning differences in alternative task movements in people with early stages PD vs. age-matched healthy controls (HCs). Thirty participants with PD (67.6 ± 8.0 yrs, 1.9 ± 0.7 H&Y) and 30 HC (69.6 ± 5.2 yrs) performed three Serial Reaction Time Tasks (SRTTs), which differed for the body segment used to perform it: Hands, Arms, and Feet testing. Visual-motor reaction time was recorded in response to visual stimuli. For each task, eight blocks, each consisting of 4 repetitions of a 12-stimuli sequence, were presented: random sequence order was practiced in block one and eight (R1, R8); a fixed 12-stimuli sequence order was performed in blocks from 2 to 7 (S2-S7). Reaction Times were corrected for errors (RT) and reprocessed as percentage values, where the mean RT of R1 represented 100% (RT_R1%). RT at R1 (RT_R1), overall RT (RT_global) and the magnitude of implicit motor learning (_iML, calculated as % difference between R8 and S7) were compared among body segments and PD vs. HCs groups by mixed two-way RM-ANOVA. The learning responses (RT_R1−R8Curve) and the errors curves were compared among body segments, groups and blocks (R1, R8 and S2-S7) by mixed three-way RM-ANOVA. While PD patients showed the characteristic bradykinesia, our data indicate that they are able to gain an identical sequence-specific implicit motor learning magnitude, with a similar timing, compared to HCs. Moreover, the magnitude and timing of the learning response is consistent across different body segments. This suggests that SRTT, conducted with different body segments, could be considered a valuable tool for studying motor learning in PD and HCs in neurorehabilitation.

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.

Advancements in sensor technologies capable of measuring key biomarkers can greatly enhance medical diagnostics and health monitoring, facilitating more effective diagnosis, treatment, and prevention. While there are numerous biomarkers of interest, gaseous ammonia (NH₃) stands out due to its significance as an indicator of organ function (e.g., liver and kidney failure) or bacterial infections (e.g., Helicobacter pylori, Proteus mirabilis) in the body. This review discusses the metabolism and presence of NH₃ in body organs, as well as clinical conditions associated with the regulation of NH₃ concentrations. This paper reviews various sensing materials and parameters, along with current state‐of‐the‐art gas sensors capable of detecting NH₃ for biomedical applications. Additionally, it addresses the challenges faced by biomedical sensing technologies already in use in medical applications. This review highlights that while NH₃ sensing applications for breath analysis are already well‐developed, in vivo NH₃ sensing remains in its early stages and faces significant challenges. Finally, promising future research directions for NH₃ sensing are discussed, including the integration of NH₃ sensors in catheters or in the Simulator of Human Intestinal Microbial Ecosystem, as well as the early detection of antibiotic‐resistant bacteria to enhance medical diagnostics.

The maximal rate of fat oxidation (MFO, in g∙min⁻¹) and the relative exercise intensity at which it occurs (FAT_max, as %V̇O_2max) are indexes of metabolic flexibility. The time-consuming, graded exercise protocol required for MFO/FAT_max determination hinders the extensive use of these indexes for individualized exercise prescription and monitoring. Purpose: validate ramp testing for MFO and FAT_max measures in postmenopausal women. Methods: Seventeen healthy women (age: 54 ± 4 years, BMI 22 ± 3 kg·m⁻², and V̇O_2max 36.4 ± 5.3 mL·min⁻¹), who were 4 ± 3 years from menopause, performed on a cycle-ergometer, a ramp, and a graded incremental test. Based on V̇O₂ and respiratory exchange ratio from the ramp and graded protocol (i.e., the 5th minute of each step), MFO and FAT_max were determined. Data from the two protocols were compared using paired t-tests, linear regression, and Bland–Altman analysis. Results: The MFO measured with a ramp protocol was not different from (0.24 ± 0.09 vs. 0.20 ± 0.08 g·min⁻¹, p = 0.10), and moderately associated with, that of the graded protocol (r² = 0.46). FAT_max occurred at similar exercise intensity for both protocols (47.8 ± 5.1 vs. 47.5 ± 4.3 %V̇O_2max, p = 0.91, r² = 0.52). The comparison of MFO and FAT_max across the protocols yields a non-significant bias but a relatively large limit of agreement (respectively, 0.05 g·min⁻¹, LOA = −0.08, and 0.19 g·min⁻¹; 0.3 %V̇O_2max, LOA = −7.8, and 10.6 %V̇O_2max). Conclusions: In postmenopausal women, ramp testing offers a valid alternative to the graded protocol for identifying MFO and FAT_max. The availability of a time- and cost-efficient approach, which can be incorporated into standard ramp incremental testing, can facilitate using these indexes of metabolic flexibility in research and medicine.

We investigated whether pedal cadence (60 vs. 100 rpm) affects oxygen uptake (V̇O₂) and power output (PO) at two indexes of the heavy-to-severe-intensity domain boundary (i.e., critical power (CP) and respiratory compensation point (RCP)) and their correspondence. Fourteen adults (7 females, 23 ± 2 years) cycled at 60 and 100 rpm during: (i) a “step-ramp-step” protocol to identify V̇O₂ and PO at RCP; (ii) 4–5 exhaustive constant-PO bouts for CP identification; and (iii) a constant-power bout at CP to identify V̇O₂ at CP. Separate two-way repeated measures Analysis of variance assessed whether V̇O₂ and PO were affected by index (CP vs. RCP) and cadence (60 vs. 100 rpm). The V̇O₂ was not affected by index (mean difference (MD) = 73 ± 197 mL·min⁻¹; p = 0.136) but there was an index × cadence interaction ( p = 0.014), such that V̇O₂ was higher at 100 versus 60 rpm for CP (MD = 142 ± 169 mL·min⁻¹; p = 0.008), but not RCP ( p = 0526). The PO was affected by cadence (MD = 13 ± 9 W; p < 0.001) and index (MD = 8 ± 11 W; p = 0.016), with no cadence × index interaction ( p = 0.168). The systematic bias in PO confirms cadence-specificity of CP and RCP. The relationship between these indexes and their change in unison in PO suggests a mechanistic link between these two heavy-to-severe domain boundary candidates.

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.