Metabolic Rate in Adolescent Athletes: The Development and Validation of New Equations, and Comparison to Previous Models.

We sought to assess the accuracy of current or developing new prediction equations for resting metabolic rate (RMR) in adolescent athletes. RMR was assessed via indirect calorimetry, alongside known predictors (body composition via dual-energy X-ray absorptiometry, height, age, and sex) and hypothesized predictors (race and maturation status assessed via years to peak height velocity), in a diverse cohort of adolescent athletes (n = 126, 77% male, body mass = 72.8 ± 16.6 kg, height = 176.2 ± 10.5 cm, age = 16.5 ± 1.4 years). Predictive equations were produced and cross-validated using repeated k-fold cross-validation by stepwise multiple linear regression (10 folds, 100 repeats). Performance of the developed equations was compared with several published equations. Seven of the eight published equations examined performed poorly, underestimating RMR in >75% to >90% of cases. Root mean square error of the six equations ranged from 176 to 373, mean absolute error ranged from 115 to 373 kcal, and mean absolute error SD ranged from 103 to 185 kcal. Only the Schofield equation performed reasonably well, underestimating RMR in 51% of cases. A one- and two-compartment model were developed, both r2 of .83, root mean square error of 147, and mean absolute error of 114 ± 26 and 117 ± 25 kcal for the one- and two-compartment model, respectively. Based on the models' performance, as well as visual inspection of residual plots, the following model predicts RMR in adolescent athletes with better precision than previous models; RMR = 11.1 × body mass (kg) + 8.4 × height (cm) - (340 male or 537 female).

Accuracy of a smart bottle in measuring fluid intake by American football players during pre-season training.

Rehydration is important for athlete performance and recovery. However, it can be challenging to follow appropriate fluid replacement practices due in part to difficulties in tracking fluid intake in real time. The purpose of this study was to determine the accuracy of a smart bottle in measuring fluid intake during exercise. Thirty male American football athletes drank from bottles equipped with a smart cap during outdoor pre-season practices (110 ± 30 min; 29.3 ± 3.0 °C; 75 ± 11% rh). The cap technology included optical sensors, microprocessors, batteries, and wireless connectivity that transmitted fluid volume data to a smartphone application in real-time. Reference measurements of fluid intake from the smart bottle were determined by gravimetry followed by conversion to volume using the density of the fluid consumed. There was no significant mean difference in fluid intake between the smart bottle and reference method (1220 ± 371 ml vs. 1236 ± 389 ml, p = 0.39 paired t test). Bland-Altman 95% limits of agreement between methods was - 212 to 180 ml. The smart bottle provided accurate measurements of fluid intake during exercise in real-world field conditions on a group level and within limits of agreement of - 212 to 180 ml (or approximately ± 15% of overall fluid intake) on an individual level.

Skin-interfaced microfluidic system with personalized sweating rate and sweat chloride analytics for sports science applications.

Advanced capabilities in noninvasive, in situ monitoring of sweating rate and sweat electrolyte losses could enable real-time personalized fluid-electrolyte intake recommendations. Established sweat analysis techniques using absorbent patches require post-collection harvesting and benchtop analysis of sweat and are thus impractical for ambulatory use. Here, we introduce a skin-interfaced wearable microfluidic device and smartphone image processing platform that enable analysis of regional sweating rate and sweat chloride concentration ([Cl-]). Systematic studies (n = 312 athletes) establish significant correlations for regional sweating rate and sweat [Cl-] in a controlled environment and during competitive sports under varying environmental conditions. The regional sweating rate and sweat [Cl-] results serve as inputs to algorithms implemented on a smartphone software application that predicts whole-body sweating rate and sweat [Cl-]. This low-cost wearable sensing approach could improve the accessibility of physiological insights available to sports scientists, practitioners, and athletes to inform hydration strategies in real-world ambulatory settings.