Carlos Balsalobre-Fernández

The validity and reliability of an iPhone app for measuring vertical jump performance

Abstract The purpose of this investigation was to analyse the concurrent validity and reliability of an iPhone app (called: My Jump) for measuring vertical jump performance. Twenty recreationally active healthy men (age: 22.1 ± 3.6 years) completed five maximal countermovement jumps, which were evaluated using a force platform (time in the air method) and a specially designed iPhone app. My jump was developed to calculate the jump height from flight time using the high-speed video recording facility on the iPhone 5 s. Jump heights of the 100 jumps measured, for both devices, were compared using the intraclass correlation coefficient, Pearson product moment correlation coefficient (r), Cronbach’s alpha (α), coefficient of variation and Bland–Altman plots. There was almost perfect agreement between the force platform and My Jump for the countermovement jump height (intraclass correlation coefficient = 0.997, P < 0.001; Bland–Altman bias = 1.1 ± 0.5 cm, P < 0.001). In comparison with the force platform, My Jump showed good validity for the CMJ height (r = 0.995, P < 0.001). The results of the present study showed that CMJ height can be easily, accurately and reliably evaluated using a specially developed iPhone 5 s app.

Intersession and intrasession reliability and validity of the my jump app for measuring different jump actions in trained male and female athletes

Abstract Gallardo-Fuentes, F, Gallardo-Fuentes, J, Ramírez-Campillo, R, Balsalobre-Fernández, C, Martínez, C, Caniuqueo, A, Cañas, R, Banzer, W, Loturco, I, Nakamura, FY, and Izquierdo, M. Intersession and intrasession reliability and validity of the My Jump app for measuring different jump actions in trained male and female athletes. J Strength Cond Res 30(7): 2049–2056, 2016—The purpose of this study was to analyze the concurrent validity and reliability of the iPhone app named My Jump for measuring jump height in 40-cm drop jumps (DJs), countermovement jumps (CMJs), and squat jumps (SJs). To do this, 21 male and female athletes (age, 22.1 ± 3.6 years) completed 5 maximal DJs, CMJs, and SJs on 2 separate days, which were evaluated using a contact platform and the app My Jump, developed to calculate jump height from flight time using the high-speed video recording facility on the iPhone. A total of 630 jumps were compared using the intraclass correlation coefficient (ICC), Bland-Altman plots, Pearsons product moment correlation coefficient (r), Cronbachs alpha (&agr;), and coefficient of variation (CV). There was almost perfect agreement between the measurement instruments for all jump height values (ICC = 0.97–0.99), with no differences between the instruments (p > 0.05; mean difference of 0.2 cm). Almost perfect correlation was observed between the measurement instruments for SJs, CMJs, and DJs (r = 0.96–0.99). My Jump showed very good within-subject reliability (&agr; = 0.94–0.99; CV = 3.8–7.6) and interday reliability (r = 0.86–0.95) for SJs, CMJs, and DJs in all subjects. Therefore, the iPhone app named My Jump provides reliable intersession and intrasession data, as well as valid measurements for maximal jump height during fast (i.e., DJs) and slow (i.e., CMJs) stretch-shortening cycle muscle actions, and during concentric-only explosive muscle actions (i.e., SJs), in both male and female athletes in comparison with a professional contact platform.

Effects of Strength Training on Running Economy in Highly Trained Runners: A Systematic Review With Meta-analysis of Controlled Trials

Abstract Balsalobre-Fernández, C, Santos-Concejero, J, and Grivas, GV. Effects of strength training on running economy in highly trained runners: a systematic review with meta-analysis of controlled trials. J Strength Cond Res 30(8): 2361–2368, 2016—The purpose of this study was to perform a systematic review and meta-analysis of controlled trials to determine the effect of strength training programs on the running economy (RE) of high-level middle- and long-distance runners. Four electronic databases were searched in September 2015 (PubMed, SPORTDiscus, MEDLINE, and CINAHL) for original research articles. After analyzing 699 resultant original articles, studies were included if the following criteria were met: (a) participants were competitive middle- or long-distance runners; (b) participants had a V[Combining Dot Above]O2max >60 ml·kg−1·min−1; (c) studies were controlled trials published in peer-reviewed journals; (d) studies analyzed the effects of strength training programs with a duration greater than 4 weeks; and (e) RE was measured before and after the strength training intervention. Five studies met the inclusion criteria, resulting in a total sample size of 93 competitive, high-level middle- and long-distance runners. Four of the 5 included studies used low to moderate training intensities (40–70% one repetition maximum), and all of them used low to moderate training volume (2–4 resistance lower-body exercises plus up to 200 jumps and 5–10 short sprints) 2–3 times per week for 8–12 weeks. The meta-analyzed effect of strength training programs on RE in high-level middle- and long-distance runners showed a large, beneficial effect (standardized mean difference [95% confidence interval] = −1.42 [−2.23 to −0.60]). In conclusion, a strength training program including low to high intensity resistance exercises and plyometric exercises performed 2–3 times per week for 8–12 weeks is an appropriate strategy to improve RE in highly trained middle- and long-distance runners.

Validity and Reliability of the push Wearable Device to Measure Movement Velocity During the Back Squat Exercise

Abstract Balsalobre-Fernández, C, Kuzdub, M, Poveda-Ortiz, P, and Campo-Vecino, Jd. Validity and reliability of the PUSH wearable device to measure movement velocity during the back squat exercise. J Strength Cond Res 30(7): 1968–1974, 2016—The purpose of this study was to analyze the validity and reliability of a wearable device to measure movement velocity during the back squat exercise. To do this, 10 recreationally active healthy men (age = 23.4 ± 5.2 years; back squat 1 repetition maximum [1RM] = 83 ± 8.2 kg) performed 3 repetitions of the back squat exercise with 5 different loads ranging from 25 to 85% 1RM on a Smith Machine. Movement velocity for each of the total 150 repetitions was simultaneously recorded using the T-Force linear transducer (LT) and the PUSH wearable band. Results showed a high correlation between the LT and the wearable device mean (r = 0.85; standard error of estimate [SEE] = 0.08 m·s−1) and peak velocity (r = 0.91, SEE = 0.1 m·s−1). Moreover, there was a very high agreement between these 2 devices for the measurement of mean (intraclass correlation coefficient [ICC] = 0.907) and peak velocity (ICC = 0.944), although a systematic bias between devices was observed (PUSH peak velocity being −0.07 ± 0.1 m·s−1 lower, p ⩽ 0.05). When measuring the 3 repetitions with each load, both devices displayed almost equal reliability (Test–retest reliability: LT [r = 0.98], PUSH [r = 0.956]; ICC: LT [ICC = 0.989], PUSH [ICC = 0.981]; coefficient of variation [CV]: LT [CV = 4.2%], PUSH [CV = 5.0%]). Finally, individual load-velocity relationships measured with both the LT (R 2 = 0.96) and the PUSH wearable device (R 2 = 0.94) showed similar, very high coefficients of determination. In conclusion, these results support the use of an affordable wearable device to track velocity during back squat training. Wearable devices, such as the one in this study, could have valuable practical applications for strength and conditioning coaches.

Relationships between training load, salivary cortisol responses and performance during season training in middle and long distance runners.

Purpose Monitoring training from a multifactorial point of view is of great importance in elite endurance athletes. This study aims to analyze the relationships between indicators of training load, hormonal status and neuromuscular performance, and to compare these values with competition performance, in elite middle and long-distance runners. Method Fifteen elite middle and long-distance runners (12 men, 3 women; age = 26.3±5.1 yrs) were measured for training volume, training zone and session rate of perceived exertion (RPE) (daily), countermovement jump (CMJ) and salivary free cortisol (weekly) for 39 weeks (i.e., the whole season). Competition performance was also observed throughout the study, registering the season best and worst competitions. Results Season average salivary free cortisol concentrations correlate significantly with CMJ (r = −0.777) and RPE (r = 0.551). Also, weekly averages of CMJ significantly correlates with RPE (r = −0.426), distance run (r = −0.593, p<0.001) and training zone (r = 0.437, p<0.05). Finally, it was found that the CMJ (+8.5%, g = 0.65) and the RPE (−17.6%, g = 0.94) measured the week before the best competition performance of the season were significantly different compared with the measurement conducted the week before the season’s worst competition performance. Conclusions Monitoring weekly measurements of CMJ and RPE could be recommended to control training process of such athletes in a non-invasive, field-based, systematic way.

The Effects of a Maximal Power Training Cycle on the Strength, Maximum Power, Vertical Jump Height and Acceleration of High-Level 400-Meter Hurdlers

The aim of this study was to determine the effects of a power training cycle on maximum strength, maximum power, vertical jump height and acceleration in seven high-level 400-meter hurdlers subjected to a specific training program twice a week for 10 weeks. Each training session consisted of five sets of eight jump-squats with the load at which each athlete produced his maximum power. The repetition maximum in the half squat position (RM), maximum power in the jump-squat (W), a squat jump (SJ), countermovement jump (CSJ), and a 30-meter sprint from a standing position were measured before and after the training program using an accelerometer, an infra-red platform and photocells. The results indicated the following statistically significant improvements: a 7.9% increase in RM (Z=-2.03, p=0.021, δc=0.39), a 2.3% improvement in SJ (Z=-1.69, p=0.045, δc=0.29), a 1.43% decrease in the 30-meter sprint (Z=-1.70, p=0.044, δc=0.12), and, where maximum power was produced, a change in the RM percentage from 56 to 62% (Z=-1.75, p=0.039, δc=0.54). As such, it can be concluded that strength training with a maximum power load is an effective means of increasing strength and acceleration in high-level hurdlers.

Load–velocity profiling in the military press exercise: Effects of gender and training

This study aimed (1) to analyze the accuracy of mean propulsive velocity to predict the percentage of the 1-repetition maximum in the seated military press exercise and (2) to test the effect of gender and of a resistance training program on the load–velocity profile. The load–velocity relationships of 26 men and 13 women were evaluated by means of an incremental loading test up to the individual 1-repetition maximum. Additionally, the load–velocity relationships of 24 of those 26 men were measured again after a six-week resistance training program. Individual load–velocity relationships had very high coefficients of determination and low standard errors of the estimate (R2 = 0.987; standard error of the estimate = 0.04 m/s). Differences higher than 10% between the individual and the general load–velocity profiles as well as a high between-participants’ variability for the mean propulsive velocity attained at each 1-repetition maximum (coefficient of variation = 12.9–24.6%) were identified. The load–velocity profiles proved to be affected by both the gender (higher mean propulsive velocity at each %1-repetition maximum for men) and the resistance training program (lower mean propulsive velocity at each %1-repetition maximum after training). Taken together, these results speak in favor of creating individual profiles instead of using general equations when using the load–velocity relationship to estimate relative load.

Load, Force and Power-Velocity Relationships in the Prone Pull-Up Exercise

PURPOSE To analyze the load-, force-, and power-velocity relationships and determine the load that optimizes power output on the pull-up exercise. METHODS 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. RESULTS Almost perfect individual load-velocity (R2 = .975 ± 0.02), force-velocity (R2 = .954 ± 0.04), and power-velocity (R2 = .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. CONCLUSIONS 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.

Jump-Squat Performance and Its Relationship With Relative Training Intensity in High-Level Athletes

PURPOSE 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. METHOD 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. RESULTS Individual regression analyses showed that JH (R2 = .992 ± .005) and the jump decrease (JD) that each load produced with respect to the unloaded countermovement jump (CMJ) (R2 = .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 JSs power output was 0%RM (ie, unloaded CMJ). CONCLUSIONS 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.

The load-velocity profile differs more between men and women than between individuals with different strength levels

Abstract This study aimed to determine the suitability of the load-velocity relationship to prescribe the relative load (%1RM) in women, as well as to compare the load-velocity profile between sexes and participants with different strength levels. The load-velocity relationship of 14 men (1RM: 1.17 ± 0.19) and 14 women (1RM: 0.66 ± 0.13) were evaluated in the bench press exercise. The main findings revealed that: (I) the load-velocity relationship was always strong and linear (R2 range: 0.987–0.993), (II) a steeper load-velocity profile was observed in men compared to women (Effect size [ES]: 1.09), with men showing higher velocities for light loads (ES: − 0.81 and − 0.40 for the y-intercept and 30%1RM, respectively), but women reporting higher velocities for the heavy loads (ES: 1.14 and 1.50 at 90%1RM and 100%1RM, respectively); and (III) while the slope of the load-velocity profile was moderately steeper for weak men compared to their strong counterpart (ES: 1.02), small differences were observed between strong and weak women (ES: − 0.39). While these results support the use of the individual load-velocity relationship to prescribe the %1RM in the bench press exercise for women, they also highlight the large disparities in their load-velocity profile compared to men.