Greater male variability in daily energy expenditure develops through puberty.

There is considerably greater variation in metabolic rates between men than between women, in terms of basal, activity and total (daily) energy expenditure (EE). One possible explanation is that EE is associated with male sexual characteristics (which are known to vary more than other traits) such as musculature and athletic capacity. Such traits might be predicted to be most prominent during periods of adolescence and young adulthood, when sexual behaviour develops and peaks. We tested this hypothesis on a large dataset by comparing the amount of male variation and female variation in total EE, activity EE and basal EE, at different life stages, along with several morphological traits: height, fat free mass and fat mass. Total EE, and to some degree also activity EE, exhibit considerable greater male variation (GMV) in young adults, and then a decreasing GMV in progressively older individuals. Arguably, basal EE, and also morphometrics, do not exhibit this pattern. These findings suggest that single male sexual characteristics may not exhibit peak GMV in young adulthood, however total and perhaps also activity EE, associated with many morphological and physiological traits combined, do exhibit GMV most prominently during the reproductive life stages.

Prediction Equation for Physical Activity Energy Expenditure in 11-13-Year-Old Sri Lankan Children.

This study aimed to develop a regression equation to predict physical activity energy expenditure (PAEE) using accelerometry. Children aged 11-13 years were recruited and randomly assigned to validation (n = 54) and cross-validation (n = 25) groups. The doubly labelled water (DLW) technique was used to assess energy expenditure and accelerometers were worn by participants across the same period. A preliminary equation was developed using stepwise multiple regression analysis with sex, height, weight, body mass index, fat-free mass, fat mass and counts per minute (CPM) as independent variables. Goodness-of-fit statistics were used to select the best prediction variables. The PRESS (predicted residual error sum of squares) statistical method was used to validate the final prediction equation. The preliminary equation was cross-validated on an independent group and no significant (p > 0.05) difference was observed in the PAEE estimated from the two methods. Independent variables of the final prediction equation (PAEE = [0.001CPM] - 0.112) accounted for 70.6% of the variance. The new equation developed to predict PAEE from accelerometry was found to be valid for use in Sri Lankan children.