The purpose of this study was to assess validity and reliability of sprint performance outcomes measured with an iPhone application (named: MySprint) and existing field methods (i.e. timing photocells and radar gun). To do this, 12 highly trained male sprinters performed 6 maximal 40-m sprints during a single session which were simultaneously timed using 7 pairs of timing photocells, a radar gun and a newly developed iPhone app based on high-speed video recording. Several split times as well as mechanical outputs computed from the model proposed by Samozino et al. [(2015). A simple method for measuring power, force, velocity properties, and mechanical effectiveness in sprint running. Scandinavian Journal of Medicine & Science in Sports. https://doi.org/10.1111/sms.12490] were then measured by each system, and values were compared for validity and reliability purposes. First, there was an almost perfect correlation between the values of time for each split of the 40-m sprint measured with MySprint and the timing photocells (r = 0.989-0.999, standard error of estimate = 0.007-0.015 s, intraclass correlation coefficient (ICC) = 1.0). Second, almost perfect associations were observed for the maximal theoretical horizontal force (F), the maximal theoretical velocity (V), the maximal power (P) and the mechanical effectiveness (DRF - decrease in the ratio of force over acceleration) measured with the app and the radar gun (r = 0.974-0.999, ICC = 0.987-1.00). Finally, when analysing the performance outputs of the six different sprints of each athlete, almost identical levels of reliability were observed as revealed by the coefficient of variation (MySprint: CV = 0.027-0.14%; reference systems: CV = 0.028-0.11%). Results on the present study showed that sprint performance can be evaluated in a valid and reliable way using a novel iPhone app.

García-Ramos, A, González-Hernández, JM, Baños-Pelegrín, E, Castaño-Zambudio, A, Capelo-Ramírez, F, Boullosa, D, Haff, GG, and Jiménez-Reyes, P. Mechanical and metabolic responses to traditional and cluster set configurations in the bench press exercise. J Strength Cond Res XX(X): 000-000, 2017-This study aimed to compare mechanical and metabolic responses between traditional (TR) and cluster (CL) set configurations in the bench press exercise. In a counterbalanced randomized order, 10 men were tested with the following protocols (sets × repetitions [inter-repetition rest]): TR1: 3 × 10 (0-second), TR2: 6 × 5 (0-second), CL5: 3 × 10 (5-second), CL10: 3 × 10 (10-second), and CL15: 3 × 10 (15-second). The number of repetitions (30), interset rest (5 minutes), and resistance applied (10 repetition maximum) were the same for all set configurations. Movement velocity and blood lactate concentration were used to assess the mechanical and metabolic responses, respectively. The comparison of the first and last set of the training session revealed a significant decrease in movement velocity for TR1 (Effect size [ES]: -0.92), CL10 (ES: -0.85), and CL15 (ES: -1.08) (but not for TR2 [ES: -0.38] and CL5 [ES: -0.37]); while blood lactate concentration was significantly increased for TR1 (ES: 1.11), TR2 (ES: 0.90), and CL5 (ES: 1.12) (but not for CL10 [ES: 0.03] and CL15 [ES: -0.43]). Based on velocity loss, set configurations were ranked as follows: TR1 (-39.3 ± 7.3%) > CL5 (-20.2 ± 14.7%) > CL10 (-12.9 ± 4.9%), TR2 (-10.3 ± 5.3%), and CL15 (-10.0 ± 2.3%). The set configurations were ranked as follows based on the lactate concentration: TR1 (7.9 ± 1.1 mmol·L) > CL5 (5.8 ± 0.9 mmol·L) > TR2 (4.2 ± 0.7 mmol·L) > CL10 (3.5 ± 0.4 mmol·L) and CL15 (3.4 ± 0.7 mmol·L). These results support the use of TR2, CL10, and CL15 for the maintenance of high mechanical outputs, while CL10 and CL15 produce less metabolic stress than TR2.

This study aimed to compare mechanical, metabolic, and perceptual responses between two traditional (TR) and four cluster (CL) set configurations. In a counterbalanced randomized order, 11 men were tested with the following protocols in separate sessions (sets × repetitions [inter-repetition rest]): TR1: 3×10 [0-s]; TR2: 6×5 [0-s]; CL1: 3×10 [10-s]; CL2: 3×10 [15-s]; CL3: 3×10 [30-s]); CL4: 1×30 [15-s]). The exercise (full-squat), number of repetitions (30), inter-set rest (5 min), and resistance applied (10RM) was the same for all set configurations. Mechanical fatigue was quantified by measuring the mean propulsive velocity during each repetition, and the change in countermovement jump height observed after each set and after the whole training session. Metabolic and perceptual fatigue were assessed via the blood lactate concentration and the OMNI perceived exertion scale measured after each training set, respectively. The mechanical, metabolic, and perceptual measures of fatigue were always significantly higher for the TR1 set configuration. The two set configurations that most minimized the mechanical measures of fatigue were CL2 and CL3. Perceived fatigue did not differ between the TR2, CL1, CL2 and CL3 set configurations. The lowest lactate concentration was observed in the CL3 set configuration. Therefore, both the CL2 and CL3 set configurations can be recommended because they maximize mechanical performance. However, the CL2 set configuration presents two main advantages with respect to CL3: (1) it reduces training session duration, and (2) it promotes higher metabolic stress, which to some extent may be beneficial for inducing muscle strength and hypertrophy gains.

Morin, JB, Capelo-Ramirez, F, Rodriguez-Pérez, MA, Cross, MR, and Jimenez-Reyes, P. Individual adaptation kinetics following heavy resisted sprint training. J Strength Cond Res 36(4): 1158–1161, 2022—The aim of this study was to test individual adaptation kinetics to a high-resistance sprint training program designed to improve maximal horizontal power (Pmax), and compare the group and individual results of a classical “pre-post” analysis, and a “pre-peak” approach. Thirteen male and 9 female trained sprinters had their 30-m sprint performance and mechanical outputs assessed 1 week before (PRE), and one (POST, W1), 2 (W2), 3 (W3) and 4 (W4) weeks after a 10-week training block (10 repetitions of 20-m resisted sprints at the load associated to the apex of their velocity-power relationship: i.e., 90 ± 10% body mass on average (range: 75–112%). We observed clearly different outcomes on all variables for the PRE-POST vs. PRE-PEAK analyses. The PRE-PEAK analysis showed a larger (almost double) increase in Pmax (9.98 ± 5.27% on average, p < 0.01) than the PRE-POST (5.39 ± 5.87%, p < 0.01). Individual kinetics of post-training adaptations show that peak values were not captured in the POST (W1) assessment (generally observed at W3 and W4). Finally, the week of greatest Pmax output differed strongly among subjects, with most subjects (7/22) peaking at W4. In conclusion, after a 10-week high-resistance sprint training block, a classical 1-week-PRE to 1-week-POST assessment could not capture peak adaptation, which differed among athletes. Adopting a similar approach in practice or research should improve insight into the true effects of training stimuli on athletic capabilities.

Strength and conditioning specialists commonly deal with the quantification and selection the setting of protocols regarding resistance training intensities. Although the one repetition maximum (1RM) method has been widely used to prescribe exercise intensity, the velocity-based training (VBT) method may enable a more optimal tool for better monitoring and planning of resistance training (RT) programs. The aim of this study was to compare the effects of two RT programs only differing in the training load prescription strategy (adjusting or not daily via VBT) with loads from 50 to 80% 1RM on 1RM, countermovement (CMJ) and sprint. Twenty-four male students with previous experience in RT were randomly assigned to two groups: adjusted loads (AL) (n = 13) and non-adjusted loads (NAL) (n = 11) and carried out an 8-week (16 sessions) RT program. The performance assessment pre- and post-training program included estimated 1RM and full load-velocity profile in the squat exercise; countermovement jump (CMJ); and 20-m sprint (T20). Relative intensity (RI) and mean propulsive velocity attained during each training session (Vsession) was monitored. Subjects in the NAL group trained at a significantly faster Vsession than those in AL (p < 0.001) (0.88–0.91 vs. 0.67–0.68 m/s, with a ∼15% RM gap between groups for the last sessions), and did not achieve the maximum programmed intensity (80% RM). Significant differences were detected in sessions 3–4, showing differences between programmed and performed Vsession and lower RI and velocity loss (VL) for the NAL compared to the AL group (p < 0.05). Although both groups improved 1RM, CMJ and T20, NAL experienced greater and significant changes than AL (28.90 vs.12.70%, 16.10 vs. 7.90% and −1.99 vs. −0.95%, respectively). Load adjustment based on movement velocity is a useful way to control for highly individualised responses to training and improve the implementation of RT programs.