The purpose of this study was to examine the reliability of a new upper body medicine ball push-press (MBP-P) test. Twenty-three strength trained volunteers performed a series of supine MBP-P throws using loads representing 5% and10% of their 5RM bench press (5 repetitions at each load). Throws were performed on a force platform (2000 Hz), with medicine ball kinematic data collected using a high-speed motion capture (500 Hz). Testing was repeated after 7–10 days to quantify intertest reliability. Maximal force (Fmax), impulse at Fmax, time to Fmax, and maximum rate of force development (RFDmax) were all calculated from the force platform outputs, with maximum ball velocity (Velmax) and maximum ball acceleration (Accelmax) developed from the kinematic data. Reliability was assessed using intraclass correlation (ICC), coefficient of variation (%CV), and typical error. Medicine ball kinematic variables were more reliable (CV% = 2.6–5.3, ICC = 0.87–0.95) than the various force platform derived power variables (CV% = 7.9–26.7, ICC = 0.51–0.90). The MBP-P test produces reliable data and can be used to quantify many standard power based measures, with the key findings have implications for athletic populations requiring high velocity, light load upper body pushing power.
Mark G.L. Sayers and Stephen Bishop
Richard P. Wells, Patrick J. Bishop and Malcolm Stephens
Spinal cord trauma due to head-first collisions is not uncommon in vehicle accidents, shallow water diving, football, or ice hockey. Two approaches to evaluating potential protective devices for ice hockey are described: an evaluative tool based upon an anthropometric test dummy, and a computer simulation of axial head-first collisions. Helmets reduced the peak cervical spine loads during low velocity head-first collisions by up to 8%. It is shown that large thicknesses of appropriate padding are necessary to hold the cervical spine loads to noninjurious levels. A head-first impact of 3.0 m • sec−1 required padding deformations on the order of 94 mm to hold cervical spine loads below 2,000 N.
Matthew Pearce, Tom R.P. Bishop, Stephen Sharp, Kate Westgate, Michelle Venables, Nicholas J. Wareham and Søren Brage
Harmonization of data for pooled analysis relies on the principle of inferential equivalence between variables from different sources. Ideally, this is achieved using models of the direct relationship with gold standard criterion measures, but the necessary validation study data are often unavailable. This study examines an alternative method of network harmonization using indirect models. Starting methods were self-report or accelerometry, from which we derived indirect models of relationships with doubly labelled water (DLW)-based physical activity energy expenditure (PAEE) using sets of two bridge equations via one of three intermediate measures. Coefficients and performance of indirect models were compared to corresponding direct models (linear regression of DLW-based PAEE on starting methods). Indirect model beta coefficients were attenuated compared to direct model betas (10%–63%), narrowing the range of PAEE values; attenuation was greater when bridge equations were weak. Directly and indirectly harmonized models had similar error variance but most indirectly derived values were biased at group-level. Correlations with DLW-based PAEE were identical after harmonization using continuous linear but not categorical models. Wrist acceleration harmonized to DLW-based PAEE via combined accelerometry and heart rate sensing had the lowest error variance (24.5%) and non-significant mean bias 0.9 (95%CI: −1.6; 3.4) kJ·day−1·kg−1. Associations between PAEE and BMI were similar for directly and indirectly harmonized values, but most fell outside the confidence interval of the criterion PAEE-to-BMI association. Indirect models can be used for harmonization. Performance depends on the measurement properties of original data, variance explained by available bridge equations, and similarity of population characteristics.