Laboratory versus field testing in cricket bowling: a review of current and past practice in modelling techniques

In cricket bowling, the reconstruction of elbow angle data is of utmost importance to the bowling versus throwing argument. This paper reviews system and modelling errors inherent in three-dimensional reconstruction processes with respect to the elbow extension tolerance set by the International Cricket Council. An understanding of these factors will enable more informed decisions about the choice of in-field versus laboratory testing in the calculation of elbow extension during bowling in cricket. Although there is a paucity of objective data related to in-field and laboratory testing, biomechanists must be guided by the greater accuracy of opto-reflective than video-based systems and so testing, at least for the present, should be conducted in a laboratory setting.     

Maximal heart rate declines linearly with age independent of cardiorespiratory fitness levels

There have been many conflicting observations between the linear or curvilinear decline in maximal heart rate (HR_max) with age. The aim of this study was to determine if linear or curvilinear equations would better describe the decline in HR_max with age in individuals of differing cardiorespiratory fitness (CRF) levels. Treadmill cardiopulmonary exercise test (CPX) results from participants (1510 men and 1134 women; 18–76 years) free of overt cardiovascular disease were retrospectively examined using cross-sectional and longitudinal study designs. Participants completing ≥2 CPX with ≥1 year between test dates were included in the longitudinal analysis (325 men and 150 women). Linear and quadratic regressions were applied to age and HR_max for the whole cohort and respective CRF groups (high, moderate, and low, relative to age and gender normative values). To test for differences among linear, quadratic, and polynomial equations, the change in R² (cross-sectional analysis) and Bayesian information criterion (BIC) (longitudinal analysis) from the linear to the more complex models were calculated. The quadratic or polynomial regression in the cross-sectional analysis, marginally improved the variance in HR_max explained by age compared to the linear regression for the whole cohort (0.2%), moderate fit group (0.3%), and low fit group (0.8%). With no improvements in the high fit group. BIC did not improve for any CRF category in the longitudinal analysis. In conclusion, the minimal differences among linear, quadratic, and polynomial equations in the respective CRF groups, emphasizes the use of linear prediction equations to estimate HR_max.