A NEW METHOD FOR MEASURING KINETICS AND KINEMATICS IN FIELD RESISTED RUNNING: COMPARISON TO LABORATORY TETHERED RUNNING

The current investigation compared results of a resisted sprint device to measure running kinetics and kinematics in the field with those measured by tethered running on a treadmill. Ten male students underwent two sessions comprising two 35m tethered sprints in laboratory or track. Step length and frequency, velocity, force and power were measured for each stride and averaged at each 5m interval. Variables reliability was attested by significant ICC-A between test-retest (between 0.60 and 0.88). Kinematic variables did not present a significant difference (P between 0.09 and 0.72). Despite force and power were systematically higher in laboratory condition (P \u3c 0.001), track condition presented higher correlations between force and velocity at each stride. Track tethered running may be a useful to monitor kinetics and kinematics in track resisted running drills

Comparison among the critical velocity determined by three conventional models and anaerobic threshold in running

The aim of this study was verify the use of critical velocity (CV) determined by three conventional models for prediction of the anaerobic threshold (AnT) in running. Thirteen Brazilian armed forces soldiers (age 24.6±6.6 years; body mass 74.4±7.6Kg; and body fat 17.8±5.2%) participated of the study. The CV was measured through four different intensities of running accomplished until exhaustion (tlim), it pre-adjusted to occur between 2 and 10 minutes (speed between 15km.h-1 and 21km.h-1). The CV was determined by three mathematical models, two by linear relations (velocity versus inverse of time - VCVx1/tlim; and distance versus time relations - VCDxT) and one by hyperbolic relation (time versus velocity - VCH). The AnT was determined from incremental test on treadmill with initial speed at 7km.h-1 and increment of 1.5km.h-1 at each 3 minutes until voluntary exhaustion. Immediately after each exercise stage were collect blood samples from ear lobe to measure of lactatemia. The AnT corresponded to abrupt increase of the lactate concentration response using bi-segmented linear regression (AnTBI). The three VC determinations corresponded to 13.48±0.91km.h-1 (VCVx1/tlim), 13.04±1.12km.h-1 (VCDxT) and 12.83±0.78km.h-1 (VCH) and only the VCVx1/tlim showed statistically different of the AnTBI (12.06±1.99km.h-1). However, these VC results overestimate the speedy of AnTBI in 11.84±1.30%, 7.7±1.3% and 5.83±1.04%, respectively. Significant correlation was not found among the three VC and the AnTBI. Thus, the CV determined by three conventional models seem not be a good tool for AnT prediction in armed forces soldiers at running

Computational and Complex Network Modeling for Analysis of Sprinter Athletes’ Performance in Track Field Tests

The article of record as published may be located at https://doi.org/10.3389/fphys.2018.00843Sports and exercise today are popular for both amateurs and athletes. However, we continue to seek the best ways to analyze best athlete performances and develop specific tools that may help scientists and people in general to analyze athletic achievement. Standard statistics and cause-and-effect research, when applied in isolation, typically do not answer most scientific questions. The human body is a complex holistic system exchanging data during activities, as has been shown in the emerging field of network physiology. However, the literature lacks studies regarding sports performance, running, exercise, and more specifically, sprinter athletes analyzed mathematically through complex network modeling. Here, we propose complex models to jointly analyze distinct tests and variables from track sprinter athletes in an untargeted manner. Through complex propositions, we have incorporated mathematical and computational modeling to analyze anthropometric, biomechanics, and physiological interactions in running exercise conditions. Exercise testing associated with complex network and mathematical outputs make it possible to identify which responses may be critical during running. The physiological basis, aerobic, and biomechanics variables together may play a crucial role in performance. Coaches, trainers, and runners can focus on improving specific outputs that together help toward individuals’ goals. Moreover, our type of analysis can inspire the study and analysis of other complex sport scenarios

Short and long term effects of high-intensity interval training on hormones, metabolites, antioxidant system, glycogen concentration, and aerobic performance adaptations in rats

The purpose of the study was to investigate the effects of short and long term High-Intensity Interval Training (HIIT) on anaerobic and aerobic performance, creatinine, uric acid, urea, creatine kinase, lactate dehydrogenase, catalase, superoxide dismutase, testosterone, corticosterone, and glycogen concentration (liver, soleus, and gastrocnemius). The Wistar rats were separated in two groups: HIIT and sedentary/control (CT). The lactate minimum (LM) was used to evaluate the aerobic and anaerobic performance (AP) (Baseline, 6, and 12 weeks). The lactate peak determination consisted of two swim bouts at 13% of body weight (bw): (1) 30 s of effort; (2) 30 s of passive recovery; (3) exercise until exhaustion (AP). Tethered loads equivalent to 3.5, 4.0, 4.5, 5.0, 5.5, and 6.5% bw were performed in incremental phase. The aerobic capacity in HIIT group increased after 12 weeks (5.2 +/- 0.2% bw) in relation to Baseline (4.4 +/- 0.2% low), but not after 6 weeks (4.5 +/- 0.3% bw). The exhaustion time in HIIT group showed higher values than CT after 6 (HIIT = 58 +/- 5 s; CT = 40 +/- 7 s) and 12 weeks (HIIT = 62 +/- 7 s; CT = 49 +/- 3 s). Glycogen (mg/100 mg) increased in gastrocnemius for HIIT group after 6 weeks (0.757 +/- 0.076) and 12 weeks (1.014 +/- 0.157) in comparison to Baseline (0.358 +/- 0.024). In soleus, the HIIT increased glycogen after 6 weeks (0.738 +/- 0.057) and 12 weeks (0.709 +/- 0.085) in comparison to Baseline (0.417 +/- 0.035). The glycogen in liver increased after HIIT 12 weeks (4.079 +/- 0.319) in relation to Baseline (2.400 +/- 0.416). The corticosterone (ng/mL) in HIIT increased after 6 weeks (529.0 +/- 30.5) and reduced after 12 weeks (153.6 +/- 14.5) in comparison to Baseline (370.0 +/- 18.3). In conclusion, long term HIIT enhanced the aerobic capacity, but short term was not enough to cause aerobic adaptations. The anaerobic performance increased in HIIT short and long term compared with CT, without differences between HIIT short and long term. Furthermore, the glycogen super-compensation increased after short and long term HIIT in comparison to Baseline and CT group. The corticosterone increased after 6 weeks, but reduces after 12 weeks. No significant alterations were observed in urea, uric acid, testosterone, catalase, superoxide dismutase, sulfhydryl groups, and creatine kinase in HIIT group in relation to Baseline and CT7FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP04/01205-6; 06/58411-

Determination of Force Corresponding to Maximal Lactate Steady State in Tethered Swimming

The main aim of the present investigation was to verify if the aerobic capacity (AC) measured in tethered swimming corresponds to the maximal lactate steady state (MLSS) and its correlation with 30 min and 400m free style swimming. Twenty-five swimmers were submitted to an incremental tethered swimming test (ITS) with an initial load of 20N and increments of 10N each 3min. After each stage of 3min, the athletes had 30s of interval to blood sample collections that were used to measure blood lactate concentrations ([La-]). The ACBI was determined by the abrupt increase in [La-] versus force (F). The points obtained between [La-] versus force (N) were adjusted by an exponential curve model to determine AC corresponding to 3.5mmol.l-1 (AC3.5) and 4.0mmol.l-1 (AC4.0). After these procedures, the swimmers performed maximal efforts of 30min and 400m in free style swimming. We used the distance performed in 30min and the time performed in 400m to calculate the median velocities (i.e. V30 and V400) of these protocols. After one week, in order to measure the MLSS, nine athletes performed three 30-min tethered swimming efforts with intensities of 90, 100, and 110% of ACBI. The ANOVA one-way was used to compare the ACBI, AC3.5 and AC4.0. Correlations between ACs, and between ACs and V30 and V400 (p\u3c0.05) were determined using the Pearson’s correlation coefficient. The intensity corresponding to 100% of ACBI was similar to the MLSS. It was observed significant correlations of the aerobic capacities (i.e. ACBI, AC3.5 and AC4.0) with V30 (r\u3e0.91) and V400 (r\u3e0.63). According to our results, it is possible to conclude that the ACBI corresponds to the MLSS, and both the AC - individually determined - and the AC - determined using fixed blood lactate concentrations of 3.5 and 4.0mmol.l-1 - can be used to predict the mean velocity of 30min and 400m in free style swimming. In addition to that, the tethered swimming system can be used for aerobic development in places where official sized swimming pools are not available, such as rehabilitation clinics and health clubs

Forced Swim Reliability for Exercise Testing in Rats by a Tethered Swimming Apparatus

To assess the physical capacity of rats in forced swim tests, the animal should perform a continuous activity (CON) at the surface to avoid apnea. Bobbing movement (BOB), vigorous paddling known as climbing (CLI), and diving activity (DIV) are inadequate swimming patterns known to increase the exercise intensity variability, impairing the test reliability. Thus, the exercise work accomplished and related physiological variables, such as the blood lactate concentration, may be unreproducible in forced swim. This study aimed to verify the exercise work reproducibility in rats with a 30-min test–retest at maximal lactate steady state (MLSS) intensity using a tethered-swimming apparatus that analyzes swimming patterns by the direct measurement of swimming force. Additionally, it was determined the swimming force and duration of CON, BOB, CLI, and DIV at physiologically different exercise-intensities. The swimming force at MLSS (n = 64) was 38 ± 7 gf.Kg-1, while the blood lactate concentration was 4.2 ± 1.6 mmol.L-1. In the test–retest (N = 23), swimming force (36.6 ± 7 gf.Kg-1 vs. 36.4 ± 7 gf.Kg-1) and blood lactate concentration (4.7 ± 1.7 mmol.L-1 vs. 4.2 ± 1.7 mmol.l-1) were similar, but only the swimming force was highly correlated (0.90 and 0.31). Although it was not statistically different, the swimming force for CON tends to be slightly lower than CLI and slightly higher than BOB independently of exercise-intensity. The CON pattern predominates (∼52.8 ± 18%) at intensities below and of MLSS but BOB was the swimming pattern more often observed above MLSS-intensity (52.6 ± 18%). The present study used a tethered swimming apparatus to investigate the reliability of forced swim tests for exercise testing in rats and better understand the swimming patterns when determining the MLSS, but the results can be extended to any study that rely on forced swim for exercise testing and training. The result suggests that, at least at intensities of physiological stability, the exercise work accomplished by rats is reproducible in forced swim, but the blood lactate concentration seems to be affected by other factors, such as the apnea and stress caused by the possibility of drowning, besides the exercise-intensity

Computational and Complex Network Modeling for Analysis of Sprinter Athletes’ Performance in Track Field Tests

Sports and exercise today are popular for both amateurs and athletes. However, we continue to seek the best ways to analyze best athlete performances and develop specific tools that may help scientists and people in general to analyze athletic achievement. Standard statistics and cause-and-effect research, when applied in isolation, typically do not answer most scientific questions. The human body is a complex holistic system exchanging data during activities, as has been shown in the emerging field of network physiology. However, the literature lacks studies regarding sports performance, running, exercise, and more specifically, sprinter athletes analyzed mathematically through complex network modeling. Here, we propose complex models to jointly analyze distinct tests and variables from track sprinter athletes in an untargeted manner. Through complex propositions, we have incorporated mathematical and computational modeling to analyze anthropometric, biomechanics, and physiological interactions in running exercise conditions. Exercise testing associated with complex network and mathematical outputs make it possible to identify which responses may be critical during running. The physiological basis, aerobic, and biomechanics variables together may play a crucial role in performance. Coaches, trainers, and runners can focus on improving specific outputs that together help toward individuals’ goals. Moreover, our type of analysis can inspire the study and analysis of other complex sport scenarios

Validation of the lactate minimum test as a specific aerobic evaluation protocol for table tennis players

The purpose of this study was to validate the lactate minimum test as a specific aerobic evaluation protocol for table tennis players. Using the frequency of 72 balls·min-1 for 90 sec, an exercise-induced metabolic acidosis was determined in 8 male table tennis players. The evaluation protocol began with a frequency of 40 balls·min-1 followed by an increase of 8 balls·min-1 every 3 min until exhaustion. The mean values that corresponded to the subjects' lactate minimum (Lacmin) were equal to 53.1 ± 1.5 balls·min-1 [adjusted for the time test (Lacmin_time)] and 51.6 ± 1.6 balls·min-1 [adjusted for the frequency of balls (Lacmin_Freq)], which resulted in a high correlation between the two forms of adjustment (r = 0.96 and (P = 0.01). The mean maximum lactate steady state (MLSS) was 52.6 ± 1.6 balls·min-1. Pearson's correlations between Lacmin_time vs. MLSS and Lacmin_freq vs. MLSS were statistically significant (P = 0.03 and r = 0.86, P = 0.03 and r = 0.85, respectively). These findings indicate that the Lacmin test predicts MLSS. Therefore, it is an excellent method to obtain the athletes' anaerobic threshold. Also, there is the advantage that it can be performed in 1 day in the game area. However, the Lacmin value does not depend on the Lacpeak value