Van Hooren, B and Zolotarjova, J. The difference between countermovement and squat jump performances: a review of underlying mechanisms with practical applications. J Strength Cond Res 31(7): 2011–2020, 2017—Two movements that are widely used to monitor athletic performance are the countermovement jump (CMJ) and squat jump (SJ). Countermovement jump performance is almost always better than SJ performance, and the difference in performance is thought to reflect an effective utilization of the stretch-shortening cycle. However, the mechanisms responsible for the performance-enhancing effect of the stretch-shortening cycle are frequently undefined. Uncovering and understanding these mechanisms is essential to make an inference regarding the difference between the jumps. Therefore, we will review the potential mechanisms that explain the better performance in a CMJ as compared with a SJ. It is concluded that the difference in performance may primarily be related to the greater uptake of muscle slack and the buildup of stimulation during the countermovement in a CMJ. Elastic energy may also have a small contribution to an enhanced CMJ performance. Therefore, a larger difference between the jumps is not necessarily a better indicator of high-intensity sports performance. Although a larger difference may reflect the utilization of elastic energy in a small-amplitude CMJ as a result of a well-developed capability to co-activate muscles and quickly build up stimulation, a larger difference may also reflect a poor capability to reduce the degree of muscle slack and build up stimulation in the SJ. Because the capability to reduce the degree of muscle slack and quickly build up stimulation in the SJ may be especially important to high-intensity sports performance, training protocols might concentrate on attaining a smaller difference between the jumps.

Background: Treadmills are often used in research, clinical practice, and training. Biomechanical investigations comparing treadmill and overground running report inconsistent findings. Objective: This study aimed at comparing biomechanical outcomes between motorized treadmill and overground running. Methods: Four databases were searched until June 2019. Crossover design studies comparing lower limb biomechanics during non-inclined, non-cushioned, quasi-constant-velocity motorized treadmill running with overground running in healthy humans (18–65 years) and written in English were included. Meta-analyses and meta-regressions were performed where possible. Results: 33 studies (n = 494 participants) were included. Most outcomes did not differ between running conditions. However, during treadmill running, sagittal foot–ground angle at footstrike (mean difference (MD) − 9.8° [95% confidence interval: − 13.1 to − 6.6]; low GRADE evidence), knee flexion range of motion from footstrike to peak during stance (MD 6.3° [4.5 to 8.2]; low), vertical displacement center of mass/pelvis (MD − 1.5 cm [− 2.7 to − 0.8]; low), and peak propulsive force (MD − 0.04 body weights [− 0.06 to − 0.02]; very low) were lower, while contact time (MD 5.0 ms [0.5 to 9.5]; low), knee flexion at footstrike (MD − 2.3° [− 3.6 to − 1.1]; low), and ankle sagittal plane internal joint moment (MD − 0.4 Nm/kg [− 0.7 to − 0.2]; low) were longer/higher, when pooled across overground surfaces. Conflicting findings were reported for amplitude of muscle activity. Conclusions: Spatiotemporal, kinematic, kinetic, muscle activity, and muscle–tendon outcome measures are largely comparable between motorized treadmill and overground running. Considerations should, however, particularly be given to sagittal plane kinematic differences at footstrike when extrapolating treadmill running biomechanics to overground running. Protocol registration CRD42018083906 (PROSPERO International Prospective Register of Systematic Reviews).

Background: Velocity loss (VL) experienced in a set during resistance training is often monitored to control training volume and quantify acute fatigue responses. Accordingly, various VL thresholds are used to prescribe resistance training and target different training adaptations. However, there are inconsistencies in the current body of evidence regarding the magnitude of the acute and chronic responses to the amount of VL experienced during resistance training. Objective: The aim of this systematic review was to (1) evaluate the acute training volume, neuromuscular, metabolic, and perceptual responses to the amount of VL experienced during resistance training; (2) synthesize the available evidence on the chronic effects of different VL thresholds on training adaptations; and (3) provide an overview of the factors that might differentially influence the magnitude of specific acute and chronic responses to VL during resistance training. Methods: This review was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Five databases were searched, and studies were included if they were written in English, prescribed resistance training using VL, and evaluated at least one (1) acute training volume, neuromuscular, metabolic, or perceptual response or (2) training adaptation. Risk of bias was assessed using a modified Cochrane Collaboration’s tool for assessing the risk of bias in randomized trials. Multilevel and multivariate meta-regressions were performed where possible. Results: Eighteen acute and 19 longitudinal studies met the inclusion criteria, of which only one had more than one risk of bias item assessed as high risk. Based on the included acute studies, it seems that the number of repetitions per set, blood lactate concentration, and rating of perceived exertion generally increase, while countermovement jump height, running sprint times, and velocity against fixed loads generally decrease as VL increases. However, the magnitude of these effects seems to be influenced, among other factors, by the exercise and load used. Regarding training adaptations, VL experienced during resistance training did not influence muscle strength and endurance gains. Increases in VL were associated with increases in hypertrophy (b = 0.006; 95% confidence interval [CI] 0.001, 0.012), but negatively affected countermovement jump (b = − 0.040; 95% CI − 0.079, − 0.001), sprint (b = 0.001; 95% CI 0.001, 0.002), and velocity against submaximal load performance (b = − 0.018; 95% CI − 0.029, − 0.006). Conclusions: A graded relationship exists between VL experienced during a set and acute training volume, neuromuscular, metabolic, and perceptual responses to resistance training. However, choice of exercise, load, and individual trainee characteristics (e.g., training history) seem to modulate these relationships. The choice of VL threshold does not seem to affect strength and muscle endurance gains whereas higher VL thresholds are superior for enhancing hypertrophy, and lower VL thresholds are superior for jumping, sprinting, and velocity against submaximal loads performance. Clinical Trial Registration: The original protocol was prospectively registered (https://osf.io/q4acs/) with the Open Science Framework.

Injuries and lack of motivation are common reasons for discontinuation of running. Real-time feedback from wearables can reduce discontinuation by reducing injury risk and improving performance and motivation. There are however several limitations and challenges with current real-time feedback approaches. We discuss these limitations and challenges and provide a framework to optimise real-time feedback for reducing injury risk and improving performance and motivation. We first discuss the reasons why individuals run and propose that feedback targeted to these reasons can improve motivation and compliance. Secondly, we review the association of running technique and running workload with injuries and performance and we elaborate how real-time feedback on running technique and workload can be applied to reduce injury risk and improve performance and motivation. We also review different feedback modalities and motor learning feedback strategies and their application to real-time feedback. Briefly, the most effective feedback modality and frequency differ between variables and individuals, but a combination of modalities and mixture of real-time and delayed feedback is most effective. Moreover, feedback promoting perceived competence, autonomy and an external focus can improve motivation, learning and performance. Although the focus is on wearables, the challenges and practical applications are also relevant for laboratory-based gait retraining.

B-mode ultrasound is often used to quantify muscle architecture during movements. Our objectives were to 1) systematically review the reliability of fascicle length (FL) and pennation angles (PA) measured using ultrasound during movements involving voluntary contractions; 2) systematically review the methods used in studies reporting reliability, discuss associated challenges, and provide recommendations to improve the reliability and validity of dynamic ultrasound measurements; and 3) provide an overview of computational approaches for quantifying fascicle architecture, their validity, agreement with manual quantification of fascicle architecture, and advantages and drawbacks. Three databases were searched until June 2019. Studies among healthy human individuals aged 17–85 yr that investigated the reliability of FL or PA in lower-extremity muscles during isoinertial movements and that were written in English were included. Thirty studies ( n = 340 participants) were included for reliability analyses. Between-session reliability as measured by coefficient of multiple correlations (CMC), and coefficient of variation (CV) was FL CMC: 0.89–0.96; CV: 8.3% and PA CMC: 0.87–0.90; CV: 4.5–9.6%. Within-session reliability was FL CMC: 0.82–0.99; CV: 0.0–6.7% and PA CMC: 0.91; CV: 0.0–15.0%. Manual analysis reliability was FL CMC: 0.89–0.96; CV: 0.0–15.9%; PA CMC: 0.84–0.90; and CV: 2.0–9.8%. Computational analysis FL CMC was 0.82–0.99, and PA CV was 14.0–15.0%. Eighteen computational approaches were identified, and these generally showed high agreement with manual analysis and high validity compared with phantoms or synthetic images. B-mode ultrasound is a reliable method to quantify fascicle architecture during movement. Additionally, computational approaches can provide a reliable and valid estimation of fascicle architecture.

Purpose: Various systems are available for cardiopulmonary exercise testing (CPET), but their accuracy remains largely unexplored. We evaluate the accuracy of 15 popular CPET systems to assess respiratory variables, substrate use, and energy expenditure during simulated exercise. Cross‐comparisons were also performed during human cycling experiments (i.e., verification of simulation findings), and between‐session reliability was assessed for a subset of systems.Methods: A metabolic simulator was used to simulate breath‐by‐breath gas exchange, and the values measured by each system (minute ventilation [V̇E], breathing frequency [BF], oxygen uptake [V̇O₂], carbon dioxide production [V̇CO₂], respiratory exchange ratio [RER], energy from carbs and fats, and total energy expenditure) were compared to the simulated values to assess the accuracy. The following manufacturers (system) were assessed: COSMED (Quark CPET, K5), Cortex (MetaLyzer 3B, MetaMax 3B), Vyaire (Vyntus CPX, Oxycon Pro), Maastricht Instruments (Omnical), MGC Diagnostics (Ergocard Clinical, Ergocard Pro, Ultima), Ganshorn/Schiller (PowerCube Ergo), Geratherm (Ergostik), VO2master (VO2masterPro), PNOĒ (PNOĒ), and Calibre Biometrics (Calibre).Results: Absolute percentage errors during the simulations ranged from 1.15%–44.3% for V̇E, 1.05–3.79% for BF, 1.10%–13.3% for V̇O₂, 1.07%–18.3% for V̇CO₂, 0.62%–14.8% for RER, 5.52%–99.0% for Kcal from carbs, 5.13%–133% for Kcal from fats, and 0.59%–12.1% for total energy expenditure. Between‐session variation ranged from 0.86%–21.0% for V̇O₂ and 1.14%–20.2% for V̇CO₂, respectively.Conclusion: The error of respiratory gas variables, substrate, and energy use differed substantially between systems, with only a few systems demonstrating a consistent acceptable error. We extensively discuss the implications of our findings for clinicians, researchers and other CPET users.