The purpose of this study was to monitor the changes in breathing pattern, trunk muscle stabilization, and upper-body muscular power in Paralympic swimmers throughout a competitive season over three time points: October (T1), March (T2), and August (T3). Six top-level Paralympic swimmers voluntarily participated in this study. The Friedman test, the Bonferroni–Dunn multiple comparison post hoc analysis, and Kendall’s W concordance coefficient for the measure of effect were used. A significant difference was found in the breathing pattern, trunk stability, and upper-body power variables from the T1 to T3 season (p < .05). However, no significant changes were found in the T2 season. A long-term assessment of these fitness parameters may be of practical importance for better tailoring the training programs of top-level Paralympic swimmers.
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Luca Cavaggioni, Athos Trecroci, Damiano Formenti, Luke Hogarth, Massimiliano Tosin, and Giampietro Alberti
Enrico Perri, Carlo Simonelli, Alessio Rossi, Athos Trecroci, Giampietro Alberti, and F. Marcello Iaia
Purpose: To investigate the relationship between the training load (TL = rate of perceived exertion × training time) and wellness index (WI) in soccer. Methods: The WI and TL data were recorded from 28 subelite players (age = 20.9 [2.4] y; height = 181.0 [5.8] cm; body mass = 72.0 [4.4] kg) throughout the 2017/2018 season. Predictive models were constructed using a supervised machine learning method that predicts the WI according to the planned TL. The validity of our predictive model was assessed by comparing the classification’s accuracy with the one computed from a baseline that randomly assigns a class to an example by respecting the distribution of classes (B1). Results: A higher TL was reported after the games and during match day (MD)-5 and MD-4, while a higher WI was recorded on the following days (MD-6, MD-4, and MD-3, respectively). A significant correlation was reported between daily TL (TLMDi ) and WI measured the day after (WIMDi+1) (r = .72, P < .001). Additionally, a similar weekly pattern seems to be repeating itself throughout the season in both TL and WI. Nevertheless, the higher accuracy of ordinal regression (39% [2%]) compared with the results obtained by baseline B1 (21% [1%]) demonstrated that the machine learning approach used in this study can predict the WI according to the TL performed the day before (MD<i ). Conclusion: The machine learning technique can be used to predict the WI based on a targeted weekly TL. Such an approach may contribute to enhancing the training-induced adaptations, maximizing the players’ readiness and reducing the potential drops in performance associated with poor wellness scores.
Damiano Formenti, Luca Cavaggioni, Marco Duca, Athos Trecroci, Mattia Rapelli, Giampietro Alberti, John Komar, and Pierpaolo Iodice
Background: Recent evidence has suggested that chronic physical activities including balance exercises have positive effects on cognition, but their acute effects are still unknown. In the present study, the authors tested the hypothesis that an acute bout of balance exercise would enhance cognitive performance compared with aerobic activity. Methods: A total of 20 healthy middle-aged adults completed 2 acute 30-minute balance and moderate-intensity aerobic exercise sessions on 2 counterbalanced separate occasions. To assess cognitive functions, performance tasks in executive control, perceptual speed, and simple reaction time were tested before and immediately after each exercise session. Results: Although there were no significant interactions (time × exercise condition, P > .05), the main effects of time were significant in executive control (P < .05), perceptual speed (P < .05), and simple reaction time (P < .001), showing improvements after both exercises. Conclusions: These findings highlight that both types of exercise (aerobic, more metabolic and less cognitively demanding; balance, more cognitively and less metabolically demanding) were able to positively affect simple reaction time performance, perceptual speed, and executive control independently of physiological adjustments occurring during aerobic or balance exercise.
Paolo Gaudino, F. Marcello Iaia, Anthony J. Strudwick, Richard D. Hawkins, Giampietro Alberti, Greg Atkinson, and Warren Gregson
The aim of the current study was to identify the external-training-load markers that are most influential on session rating of perceived exertion (RPE) of training load (RPE-TL) during elite soccer training.
Twenty-two elite players competing in the English Premier League were monitored. Training-load data (RPE and 10-Hz GPS integrated with a 100-Hz accelerometer) were collected during 1892 individual training sessions over an entire in-season competitive period. Expert knowledge and a collinearity r < .5 were used initially to select the external training variables for the final analysis. A multivariateadjusted within-subjects model was employed to quantify the correlations of RPE and RPE-TL (RPE × duration) with various measures of external training intensity and training load.
Total high-speed-running (HSR; >14.4 km/h) distance and number of impacts and accelerations >3 m/s2 remained in the final multivariate model (P < .001). The adjusted correlations with RPE were r = .14, r = .09, and r = .25 for HSR, impacts, and accelerations, respectively. For RPE-TL, the correlations were r = .11, r = .45, and r = .37, respectively.
The external-load measures that were found to be moderately predictive of RPE-TL in soccer training were HSR distance and the number of impacts and accelerations. These findings provide new evidence to support the use of RPE-TL as a global measure of training load in elite soccer. Furthermore, understanding the influence of characteristics affecting RPE-TL may help coaches and practitioners enhance training prescription and athlete monitoring.