4. Discussion
REGRESSION
Most published accelerometer-based energy expenditure prediction equations have been developed and validated on Western adolescent populations. Hence, it is prudent to either cross-validate such equations on the desired study population or develop new equations to improve the accuracy of prediction. We have previously established that existing regression equations are unable to accurately estimate PAEE among the current study population [Tri-axial accelerometers provide a more accurate estimate of physical activities and their ability to predict energy expenditure is reported to be higher compared to the uniaxial and biaxial devices since the acceleration that occurs in all three axes is captured [Applying stepwise multiple regression analysis on the validation group, resulted in only CPM being identified as a significant independent variable to predict PAEE. This is comparable to the equations developed by Puyau et al. [The amount of variance (RWe constructed the final prediction equation after combining the validation and cross-validation groups and PRESS statistic was used to validate the final equation. In the final prediction equation, the PRESS residual was lower than the mean bias in the cross-validation group indicating an overestimation of energy expenditure among our study population. Similar studies have not used PRESS statistics and there is no data to compare our final prediction equation with. Zhu and colleagues [We calculated the TEE using the DLW technique with the modified Weir equation [