Cormier, P, Freitas, TT, Rubio-Arias, JÁ, and Alcaraz, PE. Complex and contrast training: Does strength and power training sequence affect performance-based adaptations in team sports? A systematic review and meta-analysis. J Strength Cond Res 34(5): 1461–1479, 2020—The aims of this meta-analysis were to examine the effects of 2 different strength and power training sequences (complex: CPX; and contrast: CNT, training) on performance-based adaptations in team sports {lower-body strength (1 repetition maximum [1RM]), vertical jump (VJ), sprinting, and change of direction (COD) ability}, as well as identify factors potentially affecting said adaptations (i.e., athlete level, type of sport, intensity, and duration). CPX is the combination training that alternates biomechanically similar high load weight training exercises with lighter load power exercises, set for set (e.g., squats followed by countermovement jumps). CNT is the combination training where all high load strength exercises are performed at the beginning of the session and all lighter load power exercises at the end. After an electronic database search (PubMed, SPORTDiscus, and WoS), a total of 27 articles were included in the meta-analysis. The effects on outcomes were expressed as standardized mean differences (SMDs). Baseline to postintervention overall results for the studied variables: (a) 1RM: large effects for CPX (SMD = 2.01, 95% confidence interval [CI] 1.18–2.84) and CNT (SMD = 1.29, 95% CI 0.61–1.98); (b) VJ: large effects for CPX (SMD = 0.88, 95% CI 0.42–1.34) and medium effects for CNT (SMD = 0.55, 95% CI 0.29–0.81); (c) sprint: large effects for CPX (SMD = −0.94, 95% CI −1.33 to −0.54) and small effects for CNT (SMD = −0.27, 95% CI −0.92 to 0.39); and (d) COD: large effects for CPX (SMD = −1.17, 95% CI −1.43 to −0.90) and medium effects for CNT (SMD = −0.68, 95% CI −1.20 to −0.15). Regarding the studies that contained a control group: (a) 1RM: large effects for CPX (SMD = 1.61, 95% CI 1.12–2.10) and CNT (SMD = 1.38, 95% CI 0.30–2.46); (b) VJ: large effects for CPX (SMD = 0.85, 95% CI 0.45–1.25) and medium for CNT (SMD = 0.50, 95% CI 0.19–0.81); (c) sprint: medium effects for CPX (SMD = −0.69, 95% CI −1.02 to −0.36) and CNT (SMD = −0.51, 95% CI −0.90 to −0.11); and (d) COD: large effects for CPX (SMD = −0.83, 95% CI −1.08 to −0.59), and there were no control groups for CNT. In conclusion, both training interventions may lead to positive performance-based adaptations in team-sports with CPX interventions potentially leading to slightly greater effects.

Cormier, P, Tsai, M-C, Meylan, C, Agar-Newman, D, Epp-Stobbe, A, Kalthoff, Z, and Klimstra, M. Concurrent validity and reliability of different technologies for sprint-derived horizontal force-velocity-power profiling. J Strength Cond Res 37(6): 1298–1305, 2023—This study evaluated the validity and reliability of common systems to assess sprint-derived horizontal force-velocity-power (FVP _H ) profile metrics. Two double constellation athlete monitoring systems (STATSports Apex, Catapult Vector S7) and one timing gate system were compared with a radar gun for the computation of FVP _H metrics. Intersystem validity was assessed using intraclass correlation coefficients (ICC), Pearson's correlation coefficients (R ² ), and Bland-Altman plots with absolute and percent agreement. Intrasystem reliability was assessed with agreement bias and ICC. STATSports demonstrated moderate agreement for F ₀, P _max, τ, and D _rf (8.62, 6.46, -9.81, and 9.96%, respectively) and good agreement for V ₀ and MSS (−2.18 and −1.62%). Catapult displayed good agreement across all metrics (F ₀, V ₀, P _max, MSS, τ, and D _rf: −0.96, −0.89, −1.85, −0.84, 0.38, and −0.27%, respectively). Timing gates demonstrated good agreement with V ₀ and MSS (−2.62 and −1.71%) and poor agreement with F ₀, P _max, τ, and D _rf (19.17, 16.64, −20.49, and 20.18%, respectively). Intrasystem reliability demonstrated good agreement (<2% bias) with very large to near-perfect ICC (0.84–0.99) for Catapult and STATSports systems. Overall, GPS/GNSS 10 Hz technology is reliable across devices and can provide moderate-to-good accuracy of FVP _H metrics in single maximal effort sprints. However, Catapult provided better agreement for more FVP _H metrics than STATSports, which may be related to differences in proprietary algorithms. Also, modeling timing gate data using current FVP _H profiling techniques results in poor bias that requires greater investigation. GPS/GNSS data can be used for FVP _H profiling, which could inform performance and rehabilitation processes.

This study aimed to systematically review training methods prescribed to develop lower-body power, determine their effectiveness for the development of lower-body mechanical power and their implementation in an annual training cycle amongst team-sport athletes. The absolute and relative outcome values of concentric mean power, peak power and mean propulsive power were extracted from 19 studies. Outcomes were assessed using baseline to post intervention percent change, effect sizes, and the level of evidence concerning the method's effectiveness. A thorough analysis of the literature indicated that, based on the high level of evidence, traditional (e.g., strength training alone) and combination training (e.g., complex and contrast) methods should be considered. Further, optimal load and velocity-based training can be implemented if coaches have access to the appropriate equipment to monitor movement velocity and mechanical power in every session. This is of particular importance in periods of the season where high volumes of technical-tactical training and congested fixture periods are present. Also, flywheel, eccentric overload and weightlifting methods have been shown to be effective although the level of evidence is low. Future research should expand on current training practices whilst adequately reporting actual training loads from sport-specific training and games alongside strength-power training protocols.

Cormier, P, Meylan, C, Agar-Newman, D, Geneau, D, Epp-Stobbe, A, Lenetsky, S, and Klimstra, M. A systematic review and meta-analysis of wearable satellite system technology for linear sprint profiling: technological innovations and practical applications. J Strength Cond Res 38(2): 405–418, 2024—An emerging and promising practice is the use of global navigation satellite system (GNSS) technology to profile team-sports athletes in training and competition. Therefore, the purpose of this narrative systematic review with meta-analysis was to evaluate the literature regarding satellite system sensor usage for sprint modeling and to consolidate the findings to evaluate its validity and reliability. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, an electronic search of the databases, PubMed and SPORTDiscus (EBSCO), was conducted. Concurrent validity and reliability studies were considered, and 16 studies were retained for the review from the initial 1,485 studies identified. The effects on outcomes were expressed as standardized mean differences (SMDs, Cohen's d) for each outcome (i.e., maximal sprint speed [MSS], the acceleration constant [τ], maximal theoretical velocity [V ₀], relative force [F ₀], and relative power [P_max]). Effect magnitudes represented the SMD between GNSS-derived and criterion-derived (i.e., radar and laser) and resulted in the following estimates: small for MSS (d = 0.22, 95% CI 0.01 to 0.42), τ (d = −0.18, 95% CI −0.60 to 0.23), V ₀ (d = 0.14, 95% CI −0.08 to 0.36), relative F ₀ (d = 0.15, 95% CI −0.25 to 0.55), and relative P_max (d = 0.21, 95% CI −0.16 to 0.58). No publication bias was identified in meta-analyzed studies and moderator analysis revealed that several factors (sampling rate and sensor manufacturer) influenced the results. Heterogeneity between studies was considered moderate to high. This highlighted the differences between studies in sensor technology differences (i.e., sampling rate, sensor fusion, and satellite network acquisition), processing techniques, criterion technology used, sprint protocols, outcome reporting, and athlete characteristics. These findings may be useful in guiding improvements in sprint modeling using GNSS technology and enable more direct comparisons in future research. Implementation of all-out linear sprint efforts with GNSS technology can be integrated into sport-specific sessions for sprint modeling when robust and consistent data processing protocols are performed, which has important implications for fatigue monitoring, program design, systematic testing, and rehabilitation in individual and team sports.