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.

Objective. To explore how lifestyle and demographic, socioeconomic, and disease-related factors are associated with supervised exercise adherence in an osteoarthritis (OA) management program and the ability of these factors to explain exercise adherence.Methods. A cohort register-based study on participants from the Swedish Osteoarthritis Registry who attended the exercise part of a nationwide Swedish OA management program. We ran a multinomial logistic regression to determine the association of exercise adherence with the abovementioned factors. We calculated their ability to explain exercise adherence with the McFadden R 2 .Results. Our sample comprises 19,750 participants (73% female, mean ± SD age 67 ± 8.9 years). Among them, 5,862 (30%) reached a low level of adherence, 3,947 (20%) a medium level, and 9,941 (50%) a high level. After a listwise deletion, the analysis was run on 16,685 participants (85%), with low levels of adherence as the reference category. Some factors were positively associated with high levels of adherence, such as older age (relative risk ratio [RRR] 1.01 [95% confidence interval (95% CI) 1.01-1.02] per year), and the arthritis-specific self-efficacy (RRR 1.04 [95% CI 1.02-1.07] per 10-point increase). Others were negatively associated with high levels of adherence, such as female sex (RRR 0.82 [95% CI 0.75-0.89]), having a medium (RRR 0.89 [95% CI 0.81-0.98] or a high level of education ). Nevertheless, the investigating factors could explain 1% of the variability in exercise adherence (R 2 = 0.012).Conclusion. Despite the associations reported above, the poorly explained variability suggests that strategies based on lifestyle and demographic, socioeconomic, and disease-related factors are unlikely to improve exercise adherence significantly.

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 (FVPH) 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 FVPH metrics. Intersystem validity was assessed using intraclass correlation coefficients (ICC), Pearson's correlation coefficients (R2), and Bland-Altman plots with absolute and percent agreement. Intrasystem reliability was assessed with agreement bias and ICC. STATSports demonstrated moderate agreement for F0, Pmax, τ, and Drf (8.62, 6.46, -9.81, and 9.96%, respectively) and good agreement for V0 and MSS (−2.18 and −1.62%). Catapult displayed good agreement across all metrics (F0, V0, Pmax, MSS, τ, and Drf: −0.96, −0.89, −1.85, −0.84, 0.38, and −0.27%, respectively). Timing gates demonstrated good agreement with V0 and MSS (−2.62 and −1.71%) and poor agreement with F0, Pmax, τ, and Drf (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 FVPH metrics in single maximal effort sprints. However, Catapult provided better agreement for more FVPH metrics than STATSports, which may be related to differences in proprietary algorithms. Also, modeling timing gate data using current FVPH profiling techniques results in poor bias that requires greater investigation. GPS/GNSS data can be used for FVPH profiling, which could inform performance and rehabilitation processes.