No abstract

The purpose of the present study was to establish the intrasession and intersession reliability of variables obtained from a force plate that was used to quantitate lower extremity inter-limb asymmetry during the bilateral countermovement jump (CMJ). Secondarily, a comparison was performed to determine the influence of the jump protocol CMJ with or without an arm swing (CMJ AS and CMJ NAS, respectively) on inter-limb asymmetries. Twenty-two collegiate basketball players performed three CMJ AS and three CMJ NAS on dual force platforms during two separate testing sessions. A majority of variables met the acceptable criterion of intersession and intrasession relative reliability (ICC > 0.700), while fewer than half met standards established for absolute reliability (CV < 10%). CMJ protocol appeared to influence asymmetries; Concentric Impulse-100 ms, Eccentric Braking Rate of Force Development, Eccentric Deceleration, and Force at Zero velocity were significantly different between jumping conditions (CMJAS versus CMJ NAS; p < 0.05). The present data establish the reliability and smallest worthwhile change of inter-limb asymmetries during the CMJ, while also identifying the influence of CMJ protocol on inter-limb asymmetries, which can be useful to practitioners and clinicians in order to effectively monitor changes associated with performance, injury risk, and return-to-play strategies.

The purpose of the present investigation was to evaluate differences in Reactive Strength Index Modified (RSIMod) and Flight Time to Contraction Time Ratio (FT:CT) during the countermovement jump (CMJ) performed without the arm swing (CMJNAS) compared to the CMJ with the arm swing (CMJAS), while exploring the relationship within each variable between jump protocols. A secondary purpose sought to explore the relationship between RSIMod and FT:CT during both jump protocols. Twenty-two collegiate basketball players performed both three CMJNAS and three CMJAS on a force plate, during two separate testing sessions. RSIMod was calculated by the flight-time (RSIModFT) and impulse-momentum methods (RSIModIMP). CMJ variables were significantly greater during the CMJAS compared to CMJNAS (p < 0.001). There were large to very large correlations within each variable between the CMJAS and CMJNAS. There were significant positive correlations among RSIModFT, RSIModIMP, and FT:CT during both the CMJAS (r ≥ 0.864, p < 0.001) and CMJNAS (r ≥ 0.960, p < 0.001). These findings identify an increase in RSIMod or FT:CT during the CMJAS, that may provide independent information from the CMJNAS. In addition, either RSIMod or FT:CT may be utilized to monitor changes in performance, but simultaneous inclusion may be unnecessary.

Monitoring external training load (eTL) has become popular for team sport for managing fatigue, optimizing performance, and guiding return-to-play protocols. During indoor sports, eTL can be measured via inertial measurement units (IMU) or indoor positioning systems (IPS). Though each device provides unique information, the relationships between devices has not been examined. Therefore, the purpose of this study was to assess the association of eTL between an IMU and IPS used to monitor eTL in team sport. Retrospective analyses were performed on 13 elite male National Collegiate Athletic Association (NCAA) Division I basketball players (age: 20.2 ± 1.2 years, height: 201.1 ± 7.6 cm, mass: 96.8 ± 8.8 kg) from three practices during the off-season training phase. A one-way analysis of variance was used to test differences in eTL across practices. Pearson’s correlation examined the association between the Distance traveled during practice captured by IPS compared to PlayerLoad (PL), PlayerLoad per Minute (PL/Min), 2-Dimensional PlayerLoad (PL2D), 1-Dimensional PlayerLoad Forward (PL1D-FWD), Side (PL1D-SIDE), and Up (PL1D-UP) captured from the IMU. Regression analyses were performed to predict PL from Distance traveled. The eTL characteristics during Practice 1: PL = 420.4 ± 102.9, PL/min = 5.8 ± 1.4, Distance = 1645.9 ± 377.0 m; Practice 2: PL = 472.8 ± 109.5, PL/min = 5.1 ± 1.2, Distance = 1940.0 ± 436.3 m; Practice 3: PL = 295.1 ± 57.8, PL/min = 5.3 ± 1.0, Distance = 1198.2 ± 219.2 m. Significant (p ≤ 0.05) differences were observed in PL, PL2D, PL1D-FWD, PL1D-SIDE, PL1D-UP, and Distance across practices. Significant correlations (p ≤ 0.001) existed between Distance and PL parameters (Practice 1: r = 0.799–0.891; Practice 2: r = 0.819–0.972; and Practice 3: 0.761–0.891). Predictive models using Distance traveled accounted for 73.5–89.7% of the variance in PL. Significant relationships and predictive capacities exists between systems. Nonetheless, each system also appears to capture unique information that may still be useful to performance practitioners regarding the understanding of eTL.

Background-Prior.0 points in the device and control groups (between-group Pϭ0.10). Through 65 days, device-to-control hazard ratios were as follows: all-cause mortality, 1.05 (95% confidence interval, 0.60 to 1.82); death or heart failure hospitalization, 0.87 (95% confidence interval, 0.57 to 1.33); and heart failure hospitalization, 0.66 (95% confidence interval, 0.38 to 1.13). Major bleeds occurred in 16.5% in the device (7.3% treatment related) and 5.1% in the control (Pϭ0.05) group. Conclusions-Continuous aortic flow augmentation improved cardiac performance, improving cardiac index and pulmonary capillary wedge pressure, but statistical significance for the primary efficacy end point was not attained. Hemodynamic and clinical observations provide direction toward additional studies to further investigate the clinical effects of this treatment.