Validity and Reliability of a New Device to Measure Type of Actions in Indoor Sports.

The purpose of this study was to analyze the validity and the reliability of the intensity ranges, number of actions and changes of direction measured by a commercial inertial measurement unit. Eleven elite youth futsal players performed a circuit with different type of displacements as sprinting, running at low-medium intensity, standing up and changes of direction. Data recorded by the Overtraq system were compared with video-analyzer during the six trials of each player. Standard error mean, Intraclass Correlation Coeficient and Coefficient of variation, were calculated to analyze the reliability of the device, as well as the Root Mean Square Error and Confidence Interval with correlation of Pearson for its validity. The results reported good validity for three intensity ranges (R2>0.7) with high reliability (Intraclass Correlation Coeficient: 0.8-0.9), especially for high intensity actions (Intraclass Correlation Coeficient: 0.95, Coefficient of Variation: 3.06%). Furthermore, the validity for the number of different actions was almost perfect (96.3-100%), with only small differences regarding changes of activity (mean error: 2.0%). The Overtraq system can be considered as a valid and reliable technology for measuring and monitoring actions at different intensities and changes of direction in futsal, likewise common actions for other indoor sports.

Is Marathon Training Harder than the Ironman Training? An ECO-method Comparison.

Purpose: To compare the absolute and relative training load of the Marathon (42k) and the Ironman (IM) training in recreational trained athletes. Methods: Fifteen Marathoners and Fifteen Triathletes participated in the study. Their performance level was the same relative to the sex's absolute winner at the race. No differences were presented neither in age, nor in body weight, height, BMI, running VO2max max, or endurance training experience (p > 0.05). They all trained systematically for their respective event (IM or 42k). Daily training load was recorded in a training log, and the last 16 weeks were compared. Before this, gas exchange and lactate metabolic tests were conducted in order to set individual training zones. The Objective Load Scale (ECOs) training load quantification method was applied. Differences between IM and 42k athletes' outcomes were assessed using Student's test and significance level was set at p < 0.05. Results: As expected, Competition Time was significantly different (IM 11 h 45 min ± 1 h 54 min vs. 42k 3 h 6 min ± 28 min, p < 0.001). Similarly, Training Weekly Avg Time (IM 12.9 h ± 2.6 vs. 42k 5.2 ± 0.9), and Average Weekly ECOs (IM 834 ± 171 vs. 42k 526 ± 118) were significantly higher in IM (p < 0.001). However, the Ratio between Training Load and Training Time was superior for 42k runners when comparing ECOs (IM 65.8 ± 11.8 vs. 42k 99.3 ± 6.8) (p < 0.001). Finally, all ratios between training time or load vs. Competition Time were superior for 42k (p < 0.001) (Training Time/Race Time: IM 1.1 ± 0.3 vs. 42k 1.7 ± 0.5), (ECOs Training Load/Race Time: IM 1.2 ± 0.3 vs. 42k 2.9 ± 1.0). Conclusions: In spite of IM athletes' superior training time and total or weekly training load, when comparing the ratios between training load and training time, and training time or training load vs. competition time, the preparation of a 42k showed to be harder.

Specific Intensity for Peaking: Is Race Pace the Best Option?

BACKGROUND: The peaking period for endurance competition is characterized for a relative increase of the intensity of training, after a longer period of training relatively dominated by lower intensity and higher volume. OBJECTIVES: The present study was designed to compare physiological and 10 km performance effects of high intensity training (HIT) versus race pace interval training (RP) during peaking for competition in well-trained runners. PATIENTS AND METHODS: 13 athletes took part in the study, they were divided into two groups: HIT and RP. HIT performed short intervals at ~105% of the maximal aerobic velocity (MAV), while RP trained longer intervals at a speed of ~90% of the MAV (a speed approximating 10 km race pace). After 12 weeks of baseline training, the athletes trained for 6 weeks under one of the two peaking regimes. Subjects performed 10 km prior to and after the intervention period. The total load of training was matched between groups during the treatment phase. Subjects completed a graded treadmill running test until volitional exhaustion prior to each 10 km race. MAV was determined as the minimal velocity eliciting maximal oxygen consumption (VO2max). RESULTS: Both groups significantly improved their 10 km time (35 minutes 29 seconds ± 1 minutes 41 seconds vs 34 minutes 53 seconds ± 1 minutes 55 seconds, P < 0.01 for HIT; 35 minutes 27 seconds ± 1 minutes 40 seconds vs 34 minutes 53 seconds ± 1 minutes 18 seconds P < 0.01 for RP). VO2max increased after HIT (69 ± 3.6 vs 71.5 ± 4.2 ml.Kg(-1).min(-1), P < 0.05); while it didn't for RP (68.4 ± 6 vs 69.8 ± 3 ml.Kg(-1).min(-1), p>0.05). In contrast, running economy decreased significantly after HIT (210 ± 6 ml.Kg(-1).km(-1) vs 218 ± 9, P < 0.05). CONCLUSIONS: A 6 week period of training at either 105% of MAV or 90% of MAV yielded similar performance gains in a 10km race performed at ~90% MAV. Therefore, the physiological impact of HIT training seems to be positive for VO2max but negative for running economy.

Does polarized training improve performance in recreational runners?

PURPOSE: To quantify the impact of training-intensity distribution on 10K performance in recreational athletes. METHODS: 30 endurance runners were randomly assigned to a training program emphasizing low-intensity, sub-ventilatory-threshold (VT), polarized endurance-training distribution (PET) or a moderately high-intensity (between-thresholds) endurance-training program (BThET). Before the study, the subjects performed a maximal exercise test to determine VT and respiratory-compensation threshold (RCT), which allowed training to be controlled based on heart rate during each training session over the 10-wk intervention period. Subjects performed a 10-km race on the same course before and after the intervention period. Training was quantified based on the cumulative time spent in 3 intensity zones: zone 1 (low intensity, RCT). The contribution of total training time in each zone was controlled to have more low-intensity training in PET (±77/3/20), whereas for BThET the distribution was higher in zone 2 and lower in zone 1 (±46/35/19). RESULTS: Both groups significantly improved their 10K time (39min18s ± 4min54s vs 37min19s ± 4min42s, P < .0001 for PET; 39min24s ± 3min54s vs 38min0s ± 4min24s, P < .001 for BThET). Improvements were 5.0% vs 3.6%, ~41 s difference at post-training-intervention. This difference was not significant. However, a subset analysis comparing the 12 runners who actually performed the most PET (n = 6) and BThET (n = 16) distributions showed greater improvement in PET by 1.29 standardized Cohen effect-size units (90% CI 0.31-2.27, P = .038). CONCLUSIONS: Polarized training can stimulate greater training effects than between-thresholds training in recreational runners.

Training-intensity distribution during an ironman season: relationship with competition performance.

PURPOSE: To describe training loads during an Ironman training program based on intensity zones and observe Training-performance relationships. METHODS: Nine triathletes completed a program with the same periodization model aiming at participation in the same Ironman event. Before and during the study, subjects performed ramp-protocol tests, running, and cycling to determine aerobic (AeT) and anaerobic thresholds (AnT) through gas-exchange analysis. For swimming, subjects performed a graded lactate test to determine AeT and AnT. Training was subsequently controlled by heart rate (HR) during each training session over 18 wk. Training and the competition were both quantified based on the cumulative time spent in 3 intensity zones: zone 1 (low intensity; AnT). RESULTS: Most of training time was spent in zone 1 (68% ± 14%), whereas the Ironman competition was primarily performed in zone 2 (59% ± 22%). Significant inverse correlations were found between both total training time and training time in zone 1 vs performance time in competition (r = -.69 and -.92, respectively). In contrast, there was a moderate positive correlation between total training time in zone 2 and performance time in competition (r = .53) and a strong positive correlation between percentage of total training time in zone 2 and performance time in competition (r = .94). CONCLUSIONS: While athletes perform with HR mainly in zone 2, better performances are associated with more training time spent in zone 1. A high amount of cycling training in zone 2 may contribute to poorer overall performance.