Alejandro Pérez-Castilla, Daniel Boullosa, and Amador García-Ramos
Objective: To evaluate the sensitivity of the iLOAD® application to detect the changes in mean barbell velocity of complete sets following power- and strength-oriented resistance training (RT) programs. Methods: Twenty men were randomly assigned to a power training group (countermovement jump and bench press throw at 40% of the 1-repetition maximum [1RM]) or strength training group (back squat and bench press at 70% to 90% of 1RM). Single sets of 10 repetitions at 25% and 70% of 1RM during the back squat and bench press exercises were assessed before and after the 4-week RT programs simultaneously with the iLOAD® application and a linear velocity transducer. Results: The power training group showed a greater increment in velocity performance at the 25% of 1RM (effect size range = 0.66–1.53) and the 70% of 1RM (effect size range = 0.11–0.30). The percent change in mean velocity after the RT programs highly correlated between the iLOAD® application and the linear velocity transducer for the back squat (r range = .85–.88) and bench press (r range = .87–.93). However, the iLOAD® application revealed a 2% greater increase in mean velocity after training compared to the linear velocity transducer. Conclusions: The iLOAD® application is a cost-effective, portable, and easy-to-use tool which can be used to detect changes in mean barbell velocity after power- and strength-oriented RT programs.
Cristina Cortis, Andrea Fusco, Renato Barroso, Daniel Bok, Daniel Boullosa, Daniele Conte, and Carl Foster
Carla Cristiane Silva, Maurizio Bertollo, Felipe Fossati Reichert, Daniel Alexandre Boullosa, and Fábio Yuzo Nakamura
To examine which body position and indices present better reliability of heart rate variability (HRV) measures in children and to compare the HRV analyzed in different body positions between sexes.
Twenty eutrophic prepubertal children of each sex participated in the study. The RR intervals were recorded using a portable heart rate monitor twice a day for 7 min in the supine, sitting, and standing positions. The reproducibility was analyzed using the intraclass correlation coefficient (ICC; two way mixed) and within-subject coefficient of variation (CV).Two-way ANOVA with repeated measures was used to compare the sexes.
High levels of reproducibility were indicated by higher ICC in the root-mean-square difference of successive normal RR intervals (RMSSD: 0.93 and 0.94) and Poincaré plot of the short-term RR interval variability (SD1: 0.92 and 0.94) parameters for boys and girls, respectively, in the supine position. The ICCs were lower in the sitting and standing positions for all HRV indices. In addition, the girls presented significantly higher values than the boys for SDNN and absolute high frequency (HF; p < .05) in the supine position.
The supine position is the most reproducible for the HRV indices in both sexes, especially the vagal related indices.
Daniel A. Boullosa, José L. Tuimil, Luis M. Alegre, Eliseo Iglesias, and Fernando Lusquiños
Countermovement jump (CMJ) and maximum running speed over a distance of 20 m were evaluated for examination of the concurrent fatigue and post-activation potentiation (PAP) in endurance athletes after an incremental feld running test.
Twenty-two endurance athletes performed two attempts of CMJ on a force plate and maximum running speed test before and following the Université de Montréal Track Test (UMTT).
The results showed an improvement in CMJ height (3.6%) after UMTT that correlated with the increment in peak power (3.4%), with a concurrent peak force loss (–10.8%) that correlated with peak power enhancement. The athletes maintained their 20 m sprint performance after exhaustion. Cluster analysis reinforced the association between CMJ and peak power increments in responders with a reported correlation between peak power and sprint performance increments (r = .623; P = .041); nonresponders showed an impairment of peak force, vertical stiffness, and a higher vertical displacement of the center of mass during the countermovement that correlated with lactate concentration (r = –0.717; P = .02).
It can be suggested that PAP could counteract the peak force loss after exhaustion, allowing the enhancement of CMJ performance and the maintenance of sprint ability in endurance athletes after the UMTT. From these results, the evaluation of CMJ after incremental running tests for the assessment of muscular adaptations in endurance athletes can be recommended.
Carl Foster, Renato Barroso, Daniel Bok, Daniel Boullosa, Arturo Casado, Cristina Cortis, Jos J. de Koning, Andrea Fusco, and Thomas Haugen
Training intensity distribution is important to training program design. The zones 1 to 2 boundary can be defined by the Talk Test and the rating of perceived exertion. The zones 2 to 3 boundary can be defined by respiratory gas exchange, maximal lactate steady state, or, more simply, by critical speed (CS). The upper boundary of zone 3 is potential defined by the velocity at maximum oxygen uptake (vVO2max), although no clear strategy has emerged to categorize this intensity. This is not normally definable outside the laboratory. Purpose: This study predicts vVO2max from CS, determined from 1 (1.61 km) and 2 (3.22 km) citizen races in well-trained runners. Methods: A heterogeneous group of well-trained runners (N = 22) performed 1- and 2-mile races and were studied during submaximal and maximal treadmill running to measure oxygen uptake, allowing computation of vVO2max. This vVO2max was compared with CS. Results: vVO2max (4.82 [0.53] m·s−1) was strongly correlated with CS (4.37 [0.49] m·s−1; r = .84, standard error of estimate [SEE] = 0.132 m·s−1), 1-mile speed (5.09 [0.51] m·s−1; r = .84, SEE = 0.130 m·s−1), and 2-mile speed (4.68 [0.49] m·s−1; r = .86, SEE = 0.120 m·s−1). Conclusions: CS, calculated from 2 citizen races (or even training time trials), can be used to make reasonable estimates of vVO2max, which can be used in the design of running training programs.
Carl Foster, Jos J. de Koning, Christian Thiel, Bram Versteeg, Daniel A. Boullosa, Daniel Bok, and John P. Porcari
Background: Pacing studies suggest the distribution of effort for optimizing performance. Cross-sectional studies of 1-mile world records (WRs) suggest that WR progression includes a smaller coefficient of variation of velocity. Purpose: This study evaluates whether intraindividual pacing used by elite runners to break their own WR (1 mile, 5 km, and 10 km) is related to the evolution of pacing strategy. We provide supportive data from analysis in subelite runners. Methods: Men’s WR performances (with 400-m or 1-km splits) in 1 mile, 5 km, and 10 km were retrieved from the IAAF database (from 1924 to present). Data were analyzed relative to pacing pattern when a runner improved their own WR. Similar analyses are presented for 10-km performance in subelite runners before and after intensified training. Results: WR performance was improved in 1 mile (mean [SD]: 3:59.4 [11.2] to 3:57.2 [8.6]), 5 km (13:27 [0:33] to 13:21 [0:33]), and 10 km (28:35 [1:27] to 28:21 [1:21]). The average coefficient of variation did not change in the 1 mile (3.4% [1.8%] to 3.6% [1.6%]), 5 km (2.4% [0.9%] to 2.2% [0.8%]), or 10 km (1.4% [0.1%] to 1.5% [0.6%]) with improved WR. When velocity was normalized to the percentage mean velocity for each race, the pacing pattern was almost identical. Very similar patterns were observed in subelite runners in the 10 km. When time improved from 49:20 (5:30) to 45:56 (4:58), normalized velocity was similar, terminal RPE increased (8.4 [1.6] to 9.1 [0.8]), coefficient of variation was unchanged (4.4% [1.1%] to 4.8% [2.1%]), and VO2max increased (49.8 [7.4] to 55.3 [8.8] mL·min−1·kg−1). Conclusion: The results suggest that when runners break their own best performances, they employ the same pacing pattern, which is different from when WRs are improved in cross-sectional data.
Daniel Boullosa, João Gustavo Claudino, Jaime Fernandez-Fernandez, Daniel Bok, Irineu Loturco, Matthew Stults-Kolehmainen, Juan García-López, and Carl Foster
Purpose: Monitoring is a fundamental part of the training process to guarantee that the programmed training loads are executed by athletes and result in the intended adaptations and enhanced performance. A number of monitoring tools have emerged during the last century in sport. These tools capture different facets (eg, psychophysiological, physical, biomechanical) of acute training bouts and chronic adaptations while presenting specific advantages and limitations. Therefore, there is a need to identify what tools are more efficient in each sport context for better monitoring of training process. Methods and Results: We present and discuss the fine-tuning approach for training monitoring, which consists of identifying and combining the best monitoring tools with experts’ knowledge in different sport settings, designed to improve (1) the control of actual training loads and (2) understanding of athletes’ training adaptations. Instead of using single-tool approaches or merely subjective decision making, the identification of the best combination of monitoring tools to assist experts’ decisions in each specific context (ie, triangulation) is necessary to better understand the link between acute and chronic adaptations and their impact on health and performance. Future studies should elaborate on the identification of the best combination of monitoring tools for each specific sport setting. Conclusion: The fine-tuning monitoring approach requires the simultaneous use of several valid and practical tools, instead of a single tool, to improve the effectiveness of monitoring practices when added to experts’ knowledge.