Basilio Pueo

Beach Sprint rowing debuts at the 2028 Olympics without an established ergometer test for athlete selection. We examined the criterion validity of two ergometer protocols against Beach Sprint performance, each evaluated within its own season. In 2024, 21 athletes (14 men and 7 women) completed a 3-minute maximal effort (T3′), performed 3 months after the national championship. In 2025, 31 athletes (12 men and 19 women) completed two 2′30″ efforts separated by 6 minutes of recovery (T2×2′30″), performed approximately 2 months before the championship. The criterion was each athlete’s mean time across all rounds. T3′ correlated with mean competition times at r=−0.94 (95% confidence interval: −0.97 to −0.85), and T2×2′30″ at r=−0.73 (95% confidence interval: −0.86 to −0.50); adjusting for sex attenuated these correlations to r=−0.79 and r=−0.50, respectively. Models including sex explained 90% and 53% of the variance (standard error of the estimate: 5.3 s and 14.4 s), with leave-one-out cross-validation yielding Q 2 values of 0.87 and 0.43. Sex was an independent predictor under T3′ (p=0.034) but not T2×2′30″. The between-effort decrement in T2×2′30″ was unrelated to competition performance. Ergometer performance was associated with Beach Sprint competition times under each protocol, evaluated separately within its own season.

Elite soccer players increasingly live and train at moderate altitude, while inter-regional competition across different altitudes has become more frequent. However, evidence regarding whether transitioning from moderate altitude to sea-level competition influences match running performance remains limited. Twenty-three elite U20 male outfield players from a professional soccer club based at moderate altitude were monitored during friendly matches played at moderate altitude and sea level. Running performance was quantified using 10-Hz GPS devices, from which the following variables were derived: distance per minute (DPM), top speed (TS), high-speed running distance (HSR), sprint distance (SprintD), explosive accelerations (EAcc), and explosive decelerations (EDec). Generalized linear mixed models were used to examine the effect of competition altitude, with player names included as a random effect. Results showed that HSR (mean change; −32%; ES: −1.11), SprintD (−24%; −0.54), EAcc (−30%; −1.04), and EDec (−39%; −1.23) were substantially lower during matches played at sea level than during matches played at moderate altitude. These findings indicate that competing at sea level substantially influences match running performance in elite U20 male soccer players chronically trained at moderate altitude, particularly reducing high-intensity and explosive actions.

Surface electromyography (sEMG) is widely used for neuromuscular assessment, but the high cost of commercial systems limits accessibility in sports and rehabilitation settings. This study validated a low-cost open-source sEMG device (OLI) against a commercial field reference (SHI) during dynamic and isometric knee extensions in 36 healthy adults. Three preprocessing pipelines were tested for OLI signals: RAW, global root mean square (RMS), and cycle-centered RMS. Waveform similarity was assessed using the coefficient of multiple correlation (CMC), retaining repetitions with CMC ≥ 0.80. For valid repetitions, a calibration model (SHI = a + b × OLI) and Bland–Altman analysis were applied to min–max normalized RMS and area-under-the-curve (AUC) metrics. The global RMS pipeline showed the best overall performance, retaining 81.9% of repetitions with high shape similarity (CMC = 0.92 ± 0.04). It exhibited minimal bias in RMS (−0.69; 95% CI −1.11 to −0.27), limits of agreement of approximately ±10 normalized units, and a moderate-to-high correlation (r = 0.73; 95% CI 0.69–0.77). The calibration slope (b = 0.16; 95% CI 0.15–0.17) showed moderate within-session consistency (ICC(2,1) = 0.45). These findings indicate that, with appropriate preprocessing, the open-source system provides practically acceptable agreement with a commercial reference for characterizing relative muscle activation patterns, supporting its use in applied sports and rehabilitation contexts.

A neglected issue in reliability studies is the contribution of technical (device) error and biological variability to the standard (typical) error of measurement. These contributions can be estimated with mixed modeling when subjects are measured simultaneously with two devices on two occasions....

To investigate the use of static posturography as a tool to distinguish between non-recurrent and recurrent Parkinson's disease (PD) fallers based on a 1-year follow-up. This prospective cohort study included 48 participants (35 men and 13 women) from different PD associations. Data collected included fall history, sociodemographic and clinical information, balance tests, and static posturography. Group comparisons were conducted using Chi-square, T-test, Mann-Whitney U, and Hedge's (g) effect size. Pearson's correlation assessed associations between posturographic parameters and clinical tests. A two-step binary logistic regression was used to identify predictors of future falls. Discriminative ability was analyzed using a ROC curve, with the optimal cutoff determined by the Youden index. The mean speed of the center of pressure (eyes-open condition), along with the Hoehn and Yahr and the history of falls, was identified as a significant predictor of recurrent fallers, reporting excellent discrimination values (area under the ROC curve = 0.81) and a cutoff point of 18.1 mm/s. Logistic regression modeling accurately classified 88.2% of participants. Static posturography, specifically the mean speed of the center of pressure (eyes-open condition), demonstrated excellent discriminative ability in identifying PD patients at risk of recurrent falls in this study.

Background/Objectives: Parkinson’s disease (PD) is a neurodegenerative disorder that significantly impairs motor function, leading to mobility challenges and an increased risk of falls. Current assessment tools often inadequately measure the complexities of motor impairments associated with PD, highlighting the need for a reliable tool. This study introduces the Motor Assessment Timed Test (MATT), designed to assess functional mobility in PD patients. Methods: A cross-sectional study was conducted involving 57 participants (38 men and 19 women) aged 44 to 84, diagnosed with idiopathic PD. Participants were recruited from three PD associations and underwent a series of assessments, including MATT, to evaluate gait, balance, and dual-task performance under conditions that reflect real-life challenges faced by individuals with PD. Results: MATT demonstrated excellent reliability with intra-rater reliability (ICC = 0.99), inter-rater reliability (ICC = 0.96–0.99), and test–retest reliability (ICC = 0.93–0.99). The coefficient of variation for total time and each segment ranged from 4.73% to 13.71%, indicating consistent performance across trials. The concurrent validity showed very high correlations with established tools such as the Timed Up and Go (TUG) test (ρ = 0.86, p < 0.001) and the Berg Balance Scale (BBS) (ρ = −0.83, p < 0.001), among others. Only 7.3% of participants reported difficulties in understanding the MATT, predominantly those in advanced stages of the disease. In addition, 23.6% of participants experienced significant challenges in performing the test, particularly individuals with lower Mini-Mental State Examination (MMSE) scores (≤ 26) and more advanced disease progression. Conclusions: MATT is a promising tool for assessing motor complications in PD, offering a comprehensive evaluation of functional mobility. Its implementation in clinical practice could enhance the management of PD, facilitating tailored interventions and improving patient outcomes.

Rowing requires strength, endurance, and technique, where stroke efficiency depends on body mechanics, movement sequencing, and stroke rate, which impact speed transfer. This cross-sectional study investigates the role of kinematic differences in stroke mechanics as a key component of performance analysis in Olympic and Traditional rowing. Thirteen elite national-level female rowers (age: 26.9 ± 5.1 years; body mass: 60.6 ± 6.9 kg; height: 166.7 ± 6.7 cm) performed three stroke-rate conditions on two ergometer setups: an Olympic rowing ergometer and a modified ergometer with a static seat replicating Traditional rowing. The stroke rates included 18 spm (120 s), 24 spm (100 s), and 30 spm (60 s), with 2-minute rest intervals between sets. Kinematic data, including leg and trunk angles at the catch and finish, as well as leg, trunk, and arm velocities, were captured using an automated analysis system. A two-way repeated measures ANOVA revealed significant interactions between rowing modality and stroke rate for leg catch angle (η²=0.254; p=0.05), trunk finish angle (η²=0.352; p=0.013), leg velocity (η²=0.624; p<0.001), trunk velocity (η²=0.665; p<0.001), and arm velocity (η²=0.348; p=0.014). These findings emphasize distinct biomechanical patterns between modalities, which are crucial for technical optimization and individualized training. Performance analysis of angles and velocities provides valuable insights into improving rowing efficiency and addressing technical deficiencies in each modality.

The Q-LAC analyzer, a portable device employing near-infrared spectroscopy (NIRS), was designed to measure muscle oxygen saturation (SmO₂) during physical exercise. This study aimed to assess the reliability of the Q-LAC analyzer in determining SmO₂ levels during incremental cycling exercise. Thirteen physically active males (age 28.1 ± 5.3 y; height 181.2 ± 5.7 cm; body mass 79.9 ± 11.1 kg; BMI 24.2 ± 2.4 kg/m²) participated in this study. A submaximal incremental exercise test (SIET) on an electromagnetic cycle ergometer with a seven-day interval was performed twice. SmO₂ levels in the vastus lateralis (VL) muscle of the dominant leg were simultaneously recorded using the Q-LAC device during both tests. The study calculated the intraclass correlation coefficient (ICC), the coefficient of variation (CV), the standard error of measurement (SEM), the smallest worthwhile change (SWC), and mean detectable change (MDC). A within-within-subjects ANOVA revealed no statistically significant effects for session (F_1,12 = 0.97, p = 0.34, η² = 0.07) and the interaction between session and workload (F_4,48 = 0.19, p = 0.94, η² = 0.02). ICC values ranged from 0.72 to 0.91. Furthermore, the analysis of CV, SEM, and SWC indicated that SmO₂ measurements obtained with the Q-LAC device exhibit good reliability but marginal sensitivity. In conclusion, the portable Q-LAC analyzer provides consistent measurements of muscle oxygen saturation during low-to-moderate-intensity exercise on a cycle ergometer.

This study explores the stability of biomechanical parameters of the running stride of male trained athletes during a half-marathon competition. Using a field-based descriptive design, eight male athletes from a local training group were monitored throughout an official half-marathon race under identical conditions, assessing biomechanical parameters including ground contact time (GCT), leg spring stiffness (LSS), vertical oscillation (VO), and stride length (SL) recorded via the Stryd Summit Power Meter. A repeated measures analysis of variance (RM ANOVA) was conducted to detect significant changes in biomechanical parameters as the race progressed. Results demonstrated minimal changes in all parameters, with no significant differences observed for GCT (F = 0.96, p = 0.38), VO (F = 0.23, p = 0.87), and SL (F = 1.07, p = 0.35), and a small (η² = 0.004) yet statistically significant difference in LSS (F = 5.52, p = 0.03) between the first and second segments, indicating that athletes were able to maintain stable biomechanical parameters throughout the race. The conclusion highlights the need for personalized training programs tailored to the unique biomechanical adaptations and demands of endurance running.

Jump height tests are employed to measure lower-limb muscle power of athletic and non-athletic populations. The most popular instruments for this purpose are jump mats and, in recent years, smartphone apps, which compute jump height through the manual annotation of video recordings and recently automatically using the sound produced during the jump to extract the flight time. In a previous work, the afore-mentioned sound systems were presented by the authors in which the take-off and landing events from the audio recordings of jump executions were obtained using classical signal processing. In this work, a more precise, noise-immune, and robust system, capable of working in the most unfavorable environments, is presented. The system uses a deep neural network trained specifically for this purpose. More than 300 jumps were recorded to train and validate the network performance. The ground truth was a jump mat, providing a slightly better accuracy in quiet and medium quiet environments but excellent accuracy in noisy and complicated ones. The developed audio-based system is a trustworthy instrument for measuring jump height accurately in any kind of environment, providing a perfect measurement tool that can be accessed through a mobile phone in the form of an app.