The purpose of this investigation was to analyze the validity of an iPhone application (Runmatic) for measuring running mechanics. To do this, 96 steps from 12 different runs at speeds ranging from 2.77–5.55 m·s−1 were recorded simultaneously with Runmatic, as well as with an opto-electronic device installed on a motorized treadmill to measure the contact and aerial time of each step. Additionally, several running mechanics variables were calculated using the contact and aerial times measured, and previously validated equations. Several statistics were computed to test the validity and reliability of Runmatic in comparison with the opto-electronic device for the measurement of contact time, aerial time, vertical oscillation, leg stiffness, maximum relative force, and step frequency. The running mechanics values obtained with both the app and the opto-electronic device showed a high degree of correlation (r = .94–.99, p < .001). Moreover, there was very close agreement between instruments as revealed by the ICC (2,1) (ICC = 0.965–0.991). Finally, both Runmatic and the opto-electronic device showed almost identical reliability levels when measuring each set of 8 steps for every run recorded. In conclusion, Runmatic has been proven to be a highly reliable tool for measuring the running mechanics studied in this work.
Carlos Balsalobre-Fernández, Hovannes Agopyan and Jean-Benoit Morin
Carlos Balsalobre-Fernández, Carlos Ma Tejero-González and Juan del Campo-Vecino
The purpose of this study was to analyze the effects of high-level competition on salivary free cortisol, countermovement jump (CMJ), and rating of perceived exertion (RPE) and the relationships between these fatigue indicators in a group of elite middle- and long-distance runners.
The salivary free cortisol levels and CMJ height of 10 high-level middle- and long-distance runners (7 men, 3 women; age 27.6 ± 5.1y) competing in 800-m, 1500-m, 3000-m, or 5000-m events in the 2013 Spanish National Championships were measured throughout a 4-wk baseline period, then again before and after their respective races on the day of the competition. Athletes’ RPE was also measured after their races.
Cortisol increased significantly after the race compared with the value measured 90 min before the race (+98.3%, g = 0.82, P < .05), while CMJ height decreased significantly after the race (–3.9%, g = 0.34, P < .05). The decrease in CMJ height after the race correlates significantly with the postcompetition cortisol increase (r = .782, P < .05) and the RPE assessment (r = .762, P < .01).
Observed differences in CMJ height correlate significantly with salivary free cortisol levels and RPE of middle- and long-distance runners. These results show the suitability of the CMJ for monitoring multifactorial competition responses in high-level middle- and long-distance runners.
Javier Raya-González, Luis Suárez-Arrones, Archit Navandar, Carlos Balsalobre-Fernández and Eduardo Sáez de Villarreal
Context: As the number of injuries in young soccer players increases, an epidemiological study is the first step in improving preventive strategies. Objectives: To analyze the injury profile of a Spanish professional soccer club’s academy during 4 consecutive seasons and to examine the injury incidence across different chronological age groups. Design: Prospective cohort design. Setting: Aggregate injury and exposure data collected during 4 consecutive seasons. Participants: Three hundred nine elite male young soccer players. Main Outcomes Measures: Injuries that led to participation time missed from training and match play prospectively reported by medical or coaching staff of the club. Results: A total of 464 time-loss injuries were observed during this study period. The overall injury incidence was 2.93 injuries per 1000 hours, with higher incidence during matches than during training (10.16 vs 2.10 injuries/1000 h; rate ratio [RR] = 0.21; 95% confidence interval [CI], 0.17–0.25; P < .05), with the U14 age group presenting the lowest injury rate (2.39 injuries/1000 h; RR = 1.15–1.57; P < .05). In terms of injury severity, moderate injuries were the most frequent (1.42 injuries/1000 h). Muscle injuries were the most common type of injuries (57.7%; 2.75 injuries/1000 h; RR = 1.84–13.4; P < .05), and hamstrings (93/268) were the most affected muscle group (0.58 injuries/1000 h; RR = 1.58–2.91; P < .05). Injury incidence showed a seasonal variation as indicated by peaks in August and October. In matches, specifically, the match period between 75 and 90 minutes showed the highest injury incidence (10.29 injuries/1000 h; RR = 1.89–6.38; P < .01). Conclusions: The findings of this study suggest that specific preventive strategies must be implemented to try to reduce the injury incidence in Spanish elite young soccer players attending to the characteristics of each age group.
Mario Muñoz-López, David Marchante, Miguel A. Cano-Ruiz, José López Chicharro and Carlos Balsalobre-Fernández
To analyze the load-, force-, and power-velocity relationships and determine the load that optimizes power output on the pull-up exercise.
Eighty-two resistance-trained men (age 26.8 ± 5.0 y; pull-up 1-repetition maximum [1-RM; normalized per kg of body mass] 1.5 ± 0.34) performed 2 repetitions with 4 incremental loads (range 70–100%1-RM) in the pull-up exercise while mean propulsive velocity (MPV), force (MPF), and power (MPP) were measured using a linear transducer. Relationships between variables were studied using first- and second-order least-squares regression, and subjects were divided into 3 groups depending on their 1-RM for comparison purposes.
Almost perfect individual load-velocity (R 2 = .975 ± 0.02), force-velocity (R 2 = .954 ± 0.04), and power-velocity (R 2 = .966 ± 0.04) relationships, which allowed to determine the velocity at each %1-RM, as well as the maximal theoretical force (F0), velocity (V0), and power (Pmax) for each subject were observed. Statistically significant differences between groups were observed for F0 (P < .01) but not for MPV at each %1-RM, V0, and Pmax (P > .05). In addition, high correlations between F0 and 1-RM (r = .811) and V0 and Pmax (r = .865) were observed. Finally, the authors observed that the load that maximized MPP was 71.0% ± 6.6%1-RM.
The very high load-velocity, force-velocity, and power-velocity relationships enables estimation of 1-RM by measuring movement velocity, as well as determination of maximal force, velocity, and power capabilities. This information could be of great interest to strength and conditioning coaches who wish to monitor pull-up performance.
Pedro Jiménez-Reyes, Fernando Pareja-Blanco, Carlos Balsalobre-Fernández, Víctor Cuadrado-Peñafiel, Manuel A. Ortega-Becerra and Juan J. González-Badillo
To examine the relationship between the relative load in full squats and the height achieved in jump-squat (JS) exercises and to determine the load that maximizes the power output of high-level athletes.
Fifty-one male high-level track-and-field athletes (age 25.2 ± 4.4 y, weight 77. ± 6.2 kg, height 179.9 ± 5.6 cm) who competed in sprinting and jumping events took part in the study. Full-squat 1-repetition-maximum (1-RM) and JS height (JH) with loads from 17 to 97 kg were measured in 2 sessions separated by 48 h.
Individual regression analyses showed that JH (R 2 = .992 ± .005) and the jump decrease (JD) that each load produced with respect to the unloaded countermovement jump (CMJ) (R 2 = .992 ± 0.007) are highly correlated with the full-squat %1-RM, which means that training intensities can be prescribed using JH and JD values. The authors also found that the load that maximizes JS’s power output was 0%RM (ie, unloaded CMJ).
These results highlight the close relationship between JS performance and relative training intensity in terms of %1-RM. The authors also observed that the load that maximizes power output was 0%1-RM. Monitoring jump height during JS training could help coaches and athletes determine and optimize their training loads.
Alejandro Pérez-Castilla, Antonio Piepoli, Gabriel Garrido-Blanca, Gabriel Delgado-García, Carlos Balsalobre-Fernández and Amador García-Ramos
Objective: To compare the accuracy of different devices to predict the bench-press 1-repetition maximum (1RM) from the individual load–velocity relationship modeled through the multiple- and 2-point methods. Methods: Eleven men performed an incremental test on a Smith machine against 5 loads (45–55–65–75–85%1RM), followed by 1RM attempts. The mean velocity was simultaneously measured by 1 linear velocity transducer (T-Force), 2 linear position transducers (Chronojump and Speed4Lift), 1 camera-based optoelectronic system (Velowin), 2 inertial measurement units (PUSH Band and Beast Sensor), and 1 smartphone application (My Lift). The velocity recorded at the 5 loads (45–55–65–75–85%1RM), or only at the 2 most distant loads (45–85%1RM), was considered for the multiple- and 2-point methods, respectively. Results: An acceptable and comparable accuracy in the estimation of the 1RM was observed for the T-Force, Chronojump, Speed4Lift, Velowin, and My Lift when using both the multiple- and 2-point methods (effect size ≤ 0.40; Pearson correlation coefficient [r] ≥ .94; standard error of the estimate [SEE] ≤ 4.46 kg), whereas the accuracy of the PUSH (effect size = 0.70–0.83; r = .93–.94; SEE = 4.45–4.80 kg), and especially the Beast Sensor (effect size = 0.36–0.84; r = .50–.68; SEE = 9.44–11.2 kg), was lower. Conclusions: These results highlight that the accuracy of 1RM prediction methods based on movement velocity is device dependent, with the inertial measurement units providing the least accurate estimate of the 1RM.
Amador García-Ramos, Guy Gregory Haff, Francisco Luis Pestaña-Melero, Alejandro Pérez-Castilla, Francisco Javier Rojas, Carlos Balsalobre-Fernández and Slobodan Jaric
Purpose: This study compared the concurrent validity and reliability of previously proposed generalized group equations for estimating the bench press (BP) 1-repetition maximum (1RM) with the individualized load–velocity relationship modeled with a 2-point method. Methods: Thirty men (BP 1RM relative to body mass: 1.08 [0.18] kg·kg−1) performed 2 incremental loading tests in the concentric-only BP exercise and another 2 in the eccentric–concentric BP exercise to assess their actual 1RM and load–velocity relationships. A high velocity (≈1 m·s−1) and a low velocity (≈0.5 m·s−1) were selected from their load–velocity relationships to estimate the 1RM from generalized group equations and through an individual linear model obtained from the 2 velocities. Results: The directly measured 1RM was highly correlated with all predicted 1RMs (r = .847–.977). The generalized group equations systematically underestimated the actual 1RM when predicted from the concentric-only BP (P < .001; effect size = 0.15–0.94) but overestimated it when predicted from the eccentric–concentric BP (P < .001; effect size = 0.36–0.98). Conversely, a low systematic bias (range: −2.3 to 0.5 kg) and random errors (range: 3.0–3.8 kg), no heteroscedasticity of errors (r 2 = .053–.082), and trivial effect size (range: −0.17 to 0.04) were observed when the prediction was based on the 2-point method. Although all examined methods reported the 1RM with high reliability (coefficient of variation ≤ 5.1%; intraclass correlation coefficient ≥ .89), the direct method was the most reliable (coefficient of variation < 2.0%; intraclass correlation coefficient ≥ .98). Conclusions: The quick, fatigue-free, and practical 2-point method was able to predict the BP 1RM with high reliability and practically perfect validity, and therefore, the authors recommend its use over generalized group equations.