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.
Laird IV, RH, Elmer, DJ, Barberio, MD, Salom, LP, Lee, KA, and Pascoe, DD. Evaluation of performance improvements after either resistance training or sprint interval-based concurrent training. J Strength Cond Res 30(11): 3057-3065, 2016-The purpose of this investigation was to examine the effects of concurrent sprint interval and resistance training (CST) vs. resistance training (RT) on measures of strength, power, and aerobic fitness in recreationally active women. Twenty-eight women (20.3 ± 1.7 years; 63.0 ± 9.1; 51.1 ± 7.1 1 repetition maximum (1-RM) back squat (kg); V[Combining Dot Above]O2max: 35.4 ± 4.1 ml·kg·min) were recruited to complete an 11-week training program. Participants were matched-pair assigned to CST or RT cohorts after preliminary testing, which consisted of 1-RM back squats, maximal isometric squats, anaerobic power evaluations, and maximal oxygen consumption. All subjects trained 3 days per week with sprint-interval training occurring at least 4 hours after RT in the CST cohort. Both CST and RT resulted in significant improvements (p ≤ 0.05) in the 1-RM back squat (37.5 ± 7.8; 40.0 ± 9.6 kg), maximal isometric force (55.7 ± 51.3; 53.7 ± 36.7 kg), average peak anaerobic power testing (7.4 ± 6.2; 7.6 ± 6.4%), and zero-incline treadmill velocity, resulting in maximal oxygen consumption (1.8 ± 0.6; 0.8 ± 0.6 km·h). Only zero-incline treadmill velocity demonstrated a group-by-time interaction with a greater improvement after CST (p < 0.01). Rate of force development was not altered in either group. Results provide no evidence of interference to the adaptive process by CST. Coaches desiring improvements in strength, power, and endurance may want to evaluate how spring and high-intensity interval training might supplement programs already in place.
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).
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