Development of an upwind sailing ergometer

Purpose: To develop a sailing ergometer that accurately simulates upwind sailing exercise. Methods: A sailing ergometer that measures roll moment accompanied by a biofeedback system that allows imposing a certain quasi-isometric upwind sailing protocol (ie, 18 bouts of 90-s hiking at constantly varying hiking intensity interspersed with 10 s to tack) was developed. Ten male high-level Laser sailors performed an incremental cycling test (ICT; ie, step protocol at 80W + 40 W/3 min) and an upwind sailing test (UST). During both, heart rate (HR), oxygen uptake (VO2), ventilation (V-E), respiratory-exchange ratio, and rating of perceived exertion were measured. During UST, also the difference between the required and produced hiking moment (HM) was calculated as error score (ES). HR, VO2, and V-E were calculated relative to their peak values determined during ICT. After UST, the subjects were questioned about their opinion on the resemblance between this UST and real-time upwind sailing. Results: An average HM of 89.0% +/- 2.2% HMmax and an average ES of 4.1% +/- 1.8% HMmax were found. Mean HR, VO2, and V-E were, respectively, 80% +/- 4% HRpeak, 39.5% +/- 4.5% VO2peak, and 30.3% +/- 3.7% V-Epeak. Both HM and cardiorespiratory values appear to be largely comparable to literature reports during on-water upwind sailing. Moreover, the subjects gave the upwind sailing ergometer a positive resemblance score. Conclusions: Results suggest that this ergometer accurately simulates on-water upwind sailing exercise. As such, this ergometer could be a great help in performance diagnostics and training follow-up

Periodization of plyometrics : is there an optimal overload principle?

This study investigated the acute and chronic effects of 3 plyometric training (PT) programs with equal training loads (intensity × volume × frequency) on speed, agility, and jumping performance. Forty-four male recreational team sport athletes were either assigned to a program that increased training volume with exercises of mixed intensity (Mix), kept training volume equal and increased exercise intensity (LowHi), increased training volume and kept exercise intensity low (Low), or to a control group (Control). Subjects were trained twice a week for 8 weeks and were tested for 5- (5 m) and 10-m sprint (10 m), 5 × 10-m shuttle run (5 × 10 m), squat jump (SJ), countermovement jump without and with arm swing, and standing broad jump. Five-, 10- and 5 × 10-m performance did not change (p > 0.05) after the PT program. Jumping performance, except for SJ (p = 0.114), improved significantly (p < 0.05) in the PT groups compared with the control group. However, no mutual differences (p < 0.05) were established between plyometric groups. In addition, it was shown that a PT of high intensity was more likely to affect performance and blood inflammation markers in the following days. To conclude, PT programs following a different overload pattern, i.e., different combination of volume and intensity, but equal training load showed similar performance effects in recreationally trained men. However, before competition, a PT of low intensity is preferred over a PT of high intensity to avoid a decline in performance

Machine learning-based identification of the strongest predictive variables of winning and losing in Belgian professional soccer

This study aimed to identify the strongest predictive variables of winning and losing in the highest Belgian soccer division. A predictive machine learning model based on a broad range of variables (n = 100) was constructed, using a dataset consisting of 576 games. To avoid multicollinearity and reduce dimensionality, Variance Inflation Factor (threshold of 5) and BorutaShap were respectively applied. A total of 13 variables remained and were used to predict winning or losing using Extreme Gradient Boosting. TreeExplainer was applied to determine feature importance on a global and local level. The model showed an accuracy of 89.6% ± 3.1% (precision: 88.9%; recall: 90.1%, f1-score: 89.5%), correctly classifying 516 out of 576 games. Shots on target from the attacking penalty box showed to be the best predictor. Several physical indicators are amongst the best predictors, as well as contextual variables such as ELO -ratings, added transfers value of the benched players and match location. The results show the added value of the inclusion of a broad spectrum of variables when predicting and evaluating game outcomes. Similar modelling approaches can be used by clubs to identify the strongest predictive variables for their leagues, and evaluate and improve their current quantitative analyses

Independent associations between sedentary time, moderate-to-vigorous physical activity, cardiorespiratory fitness and cardio-metabolic health : a cross-sectional study

We aimed to study the independent associations of sedentary time (ST), moderate-to-vigorous physical activity (MVPA), and objectively measured cardiorespiratory fitness (CRF) with clustered cardio-metabolic risk and its individual components (waist circumference, fasting glucose, HDL-cholesterol, triglycerides and blood pressure). We also investigated whether any associations between MVPA or ST and clustered cardio-metabolic risk were mediated by CRF. MVPA, ST, CRF and individual cardio-metabolic components were measured in a population-based sample of 341 adults (age 53.8 +/- 8.9 years; 61% men) between 2012 and 2014. MVPA and ST were measured with the SenseWear pro 3 Armband and CRF was measured with a maximal exercise test. Multiple linear regression models and the product of coefficients method were used to examine independent associations and mediation effects, respectively. Results showed that low MVPA and low CRF were associated with a higher clustered cardio-metabolic risk (beta = -0.26 and beta = -0.43, both p<0.001, respectively). CRF explained 73% of the variance in the association between MVPA and clustered cardio-metabolic risk and attenuated this association to non-significance. After mutual adjustment for MVPA and ST, CRF was the most important risk factor for a higher clustered cardio-metabolic risk (beta = -0.39, p<0.001). In conclusion, because of the mediating role of CRF, lifestyle-interventions need to be feasible yet challenging enough to lead to increases in CRF to improve someone's cardio-metabolic health

Bioenergetics of the VO2 slow component between exercise intensity domains

During heavy and severe constant-load exercise, VO(2)displays a slow component (VO2sc) typically interpreted as a loss of efficiency of locomotion. In the ongoing debate on the underpinnings of the VO2sc, recent studies suggested that VO(2sc)could be attributed to a prolonged shift in energetic sources rather than loss of efficiency. We tested the hypothesis that the total cost of cycling, accounting for aerobic and anaerobic energy sources, is affected by time during metabolic transitions in different intensity domains. Eight active men performed 3 constant load trials of 3, 6, and 9 min in the moderate, heavy, and severe domains (i.e., respectively below, between, and above the two ventilatory thresholds). VO2, VO(2)of ventilation and lactate accumulation ([La-]) were quantified to calculate the adjusted oxygen cost of exercise (AdjO(2Eq), i.e., measured VO2 - VO(2)of ventilation + VO(2)equivalent of [La-]) for the 0-3, 3-6, and 6-9 time segments at each intensity, and compared by a two-way RM-ANOVA (time x intensity). After the transient phase, AdjO(2Eq)was unaffected by time in moderate (ml*3 min(-1)at 0-3, 0-6, 0-9 min: 2126 +/- 939 < 2687 +/- 1036, 2731 +/- 1035) and heavy (4278 +/- 1074 < 5121 +/- 1268, 5225 +/- 1123) while a significant effect of time was detected in the severe only (5863 +/- 1413 < 7061 +/- 1516 < 7372 +/- 1443). The emergence of the VO(2sc)was explained by a prolonged shift between aerobic and anaerobic energy sources in heavy (VO2 - VO(2)of ventilation: ml*3 min(-1)at 0-3, 0-6, 0-9 min: 3769 +/- 1128 < 4938 +/- 1256, 5091 +/- 1123, [La-]: 452 +/- 254 < 128 +/- 169, 79 +/- 135), while a prolonged metabolic shift and a true loss of efficiency explained the emergence of the VO(2sc)in severe

Aerobic interval training impacts muscle and brain oxygenation responses to incremental exercise

The purpose of the present study was to assess the effects of aerobic interval training on muscle and brain oxygenation to incremental ramp exercise. Eleven physically active subjects performed a 6-week interval training period, proceeded and followed by an incremental ramp exercise to exhaustion (25 W min\u20131). Throughout the tests pulmonary gas exchange and muscle (Vastus Lateralis) and brain (prefrontal cortex) oxygenation [concentration of deoxygenated and oxygenated hemoglobin, HHb and O2Hb, and tissue oxygenation index (TOI)] were continuously recorded. Following the training intervention V.O2peak had increased with 7.8 \ub1 5.0% (P &lt; 0.001). The slope of the decrease in muscle TOI had decreased (P = 0.017) 16.6 \ub1 6.4% and the amplitude of muscle HHb and totHb had increased (P &lt; 0.001) 40.4 \ub1 15.8 and 125.3 \ub1 43.1%, respectively. The amplitude of brain O2Hb and totHb had increased (P &lt; 0.05) 40.1 \ub1 18.7 and 26.8 \ub1 13.6%, respectively. The training intervention shifted breakpoints in muscle HHb, totHb and TOI, and brain O2Hb, HHb, totHb and TOI to a higher absolute work rate and V.O2 (P &lt; 0.05). The relative (in %) change in V.O2peak was significantly correlated to relative (in %) change slope of muscle TOI (r = 0.69, P = 0.011) and amplitude of muscle HHb (r = 0.72, P = 0.003) and totHb (r = 0.52, P = 0.021), but not to changes in brain oxygenation. These results indicate that interval training affects both muscle and brain oxygenation, coinciding with an increase in aerobic fitness (i.e., V.O2peak). The relation between the change in V.O2peak and muscle but not brain oxygenation suggests that brain oxygenation per se is not a primary factor limiting exercise tolerance during incremental exercise