Sweat losses in team sports can be significant due to repeated bursts of high-intensity activity, as well as the large body size of athletes, equipment and uniform requirements, and environmental heat stress often present during training and competition. In this paper we aimed to: (1) describe sweat losses and fluid balance changes reported in team sport athletes, (2) review the literature assessing the impact of hypohydration on cognitive, technical, and physical performance in sports-specific studies, (3) briefly review the potential mechanisms by which hypohydration may impact team sport performance, and (4) discuss considerations for future directions. Significant hypohydration (mean body mass loss (BML) >2%) has been reported most consistently in soccer. Although American Football, rugby, basketball, tennis, and ice hockey have reported high sweating rates, fluid balance disturbances have generally been mild (mean BML <2%), suggesting that drinking opportunities were sufficient for most athletes to offset significant fluid losses. The effect of hydration status on team sport performance has been studied mostly in soccer, basketball, cricket, and baseball, with mixed results. Hypohydration typically impaired performance at higher levels of BML (3–4%) and when the method of dehydration involved heat stress. Increased subjective ratings of fatigue and perceived exertion consistently accompanied hypohydration and could explain, in part, the performance impairments reported in some studies. More research is needed to develop valid, reliable, and sensitive sport-specific protocols and should be used in future studies to determine the effects of hypohydration and modifying factors (e.g., age, sex, athlete caliber) on team sport performance.

This study determined the relations between regional (REG) and whole body (WB) sweating rate (RSR and WBSR, respectively) as well as REG and WB sweat Na concentration ([Na]) during exercise. Twenty-six recreational athletes (17 men, 9 women) cycled for 90 min while WB sweat [Na] was measured using the washdown technique. RSR and REG sweat [Na] were measured from nine regions using absorbent patches. RSR and REG sweat [Na] from all regions were significantly ( P < 0.05) correlated with WBSR ( r = 0.58-0.83) and WB sweat [Na] ( r = 0.74-0.88), respectively. However, the slope and y-intercept of the regression lines for most models were significantly different than 1 and 0, respectively. The coefficients of determination ( r) were 0.44-0.69 for RSR predicting WBSR [best predictors: dorsal forearm ( r = 0.62) and triceps ( r = 0.69)] and 0.55-0.77 for REG predicting WB sweat [Na] [best predictors: ventral forearm ( r = 0.73) and thigh ( r = 0.77)]. There was a significant ( P < 0.05) effect of day-to-day variability on the regression model predicting WBSR from RSR at most regions but no effect on predictions of WB sweat [Na] from REG. Results suggest that REG cannot be used as a direct surrogate for WB sweating responses. Nonetheless, the use of regression equations to predict WB sweat [Na] from REG can provide an estimation of WB sweat [Na] with an acceptable level of accuracy, especially using the forearm or thigh. However, the best practice for measuring WBSR remains conventional WB mass balance calculations since prediction of WBSR from RSR using absorbent patches does not meet the accuracy or reliability required to inform fluid intake recommendations. NEW & NOTEWORTHY This study developed a body map of regional sweating rate and regional (REG) sweat electrolyte concentrations and determined the effect of within-subject (bilateral and day-to-day) and between-subject (sex) factors on the relations between REG and the whole body (WB). Regression equations can be used to predict WB sweat Na concentration from REG, especially using the forearm or thigh. However, prediction of WB sweating rate from REG sweating rate using absorbent patches does not reach the accuracy or reliability required to inform fluid intake recommendations.

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.

Team sport athletes face a variety of nutritional challenges related to recovery during the competitive season. The purpose of this article is to review nutrition strategies related to muscle regeneration, glycogen restoration, fatigue, physical and immune health, and preparation for subsequent training bouts and competitions. Given the limited opportunities to recover between training bouts and games throughout the competitive season, athletes must be deliberate in their recovery strategy. Foundational components of recovery related to protein, carbohydrates, and fluid have been extensively reviewed and accepted. Micronutrients and supplements that may be efficacious for promoting recovery include vitamin D, omega-3 polyunsaturated fatty acids, creatine, collagen/vitamin C, and antioxidants. Curcumin and bromelain may also provide a recovery benefit during the competitive season but future research is warranted prior to incorporating supplemental dosages into the athlete’s diet. Air travel poses nutritional challenges related to nutrient timing and quality. Incorporating strategies to consume efficacious micronutrients and ingredients is necessary to support athlete recovery in season.

Purpose To quantify total sweat electrolyte losses at two relative exercise intensities and determine the effect of workload on the relation between regional (REG) and whole body (WB) sweat electrolyte concentrations. Methods Eleven recreational athletes (7 men, 4 women; 71.5 ± 8.4 kg) completed two randomized trials cycling (30 °C, 44% rh) for 90 min at 45% (LOW) and 65% (MOD) of V O 2max in a plastic isolation chamber to determine WB sweat [Na + ] and [Cl − ] using the washdown technique. REG sweat [Na + ] and [Cl − ] were measured at 11 REG sites using absorbent patches. Total sweat electrolyte losses were the product of WB sweat loss (WBSL) and WB sweat electrolyte concentrations. Results WBSL (0.86 ± 0.15 vs. 1.27 ± 0.24 L), WB sweat [Na + ] (32.6 ± 14.3 vs. 52.7 ± 14.6 mmol/L), WB sweat [Cl − ] (29.8 ± 13.6 vs. 52.5 ± 15.6 mmol/L), total sweat Na + loss (659 ± 340 vs. 1565 ± 590 mg), and total sweat Cl − loss (931 ± 494 vs. 2378 ± 853 mg) increased significantly ( p < 0.05) from LOW to MOD. REG sweat [Na + ] and [Cl − ] increased from LOW to MOD at all sites except thigh and calf. Intensity had a significant effect on the regression model predicting WB from REG at the ventral wrist, lower back, thigh, and calf for sweat [Na + ] and [Cl − ]. Conclusion Total sweat Na + and Cl − losses increased by ~ 150% with increased exercise intensity. Regression equations can be used to predict WB sweat [Na + ] and [Cl − ] from some REG sites (e.g., dorsal forearm) irrespective of intensity (between 45 and 65% V O 2max ), but other sites (especially ventral wrist, lower back, thigh, and calf) require separate prediction equations accounting for workload.

Performance in many sports is at least partially dependent on motor control, coordination, decision-making, and other cognitive tasks. This review summarizes available evidence about the ingestion of selected nutrients or isolated compounds (dietary constituents) and potential acute effects on motor skill and/or cognitive performance in athletes. Dietary constituents discussed include branched-chain amino acids, caffeine, carbohydrate, cocoa flavanols, Gingko biloba, ginseng, guarana, Rhodiola rosea, sage, L-theanine, theobromine, and tyrosine. Although this is not an exhaustive list, these are perhaps the most researched dietary constituents. Caffeine and carbohydrate have the greatest number of published reports supporting their ability to enhance acute motor skill and cognitive performance in athletes. At this time, there is insufficient published evidence to substantiate the use of any other dietary constituents to benefit sports-related motor skill or cognitive performance. The optimal dose and timing of caffeine and carbohydrate intake promoting enhanced motor skill and cognitive performance remain to be identified. Valid, reliable, and sensitive batteries of motor skills and cognitive tests should be developed for use in future efficacy studies.

This study compared a field versus reference laboratory technique for extracting (syringe vs. centrifuge) and analyzing sweat [Na+] and [K+] (compact Horiba B‐722 and B‐731, HORIBA vs. ion chromatography, HPLC) collected with regional absorbent patches during exercise in a hot‐humid environment. Sweat samples were collected from seven anatomical sites on 30 athletes during 1‐h cycling in a heat chamber (33°C, 67% rh). Ten minutes into exercise, skin was cleaned/dried and two sweat patches were applied per anatomical site. After removal, one patch per site was centrifuged and sweat was analyzed with HORIBA in the heat chamber (CENTRIFUGE HORIBA) versus HPLC (CENTRIFUGE HPLC). Sweat from the second patch per site was extracted using a 5‐mL syringe and analyzed with HORIBA in the heat chamber (SYRINGE HORIBA) versus HPLC (SYRINGE HPLC). CENTRIFUGE HORIBA, SYRINGE HPLC, and SYRINGE HORIBA were highly related to CENTRIFUGE HPLC ([Na+]: ICC = 0.96, 0.94, and 0.93, respectively; [K+]: ICC = 0.87, 0.92, and 0.84, respectively), while mean differences from CENTRIFUGE HPLC were small but usually significant ([Na+]: 4.7 ± 7.9 mEql/L, −2.5 ± 9.3 mEq/L, 4.0 ± 10.9 mEq/L (all P < 0.001), respectively; [K+]: 0.44 ± 0.52 mEq/L (P < 0.001), 0.01 ± 0.49 mEq/L (P = 0.77), 0.50 ± 0.48 mEq/L (P < 0.001), respectively). On the basis of typical error of the measurement results, sweat [Na+] and [K+] obtained with SYRINGE HORIBA falls within ±15.4 mEq/L and ±0.68 mEq/L, respectively, of CENTRIFUGE HPLC 95% of the time. The field (SYRINGE HORIBA) method of extracting and analyzing sweat from regional absorbent patches may be useful in obtaining sweat [Na+] when rapid estimates in a hot‐humid field setting are needed.

BackgroundWe developed a digital dietary analysis tool for athletes (DATA) using a modified 24-h recall method and an integrated, customized nutrient database. The purpose of this study was to assess DATA’s validity and relative validity by measuring its agreement with registered dietitians’ (RDs) direct observations (OBSERVATION) and 24-h dietary recall interviews using the USDA 5-step multiple-pass method (INTERVIEW), respectively.MethodsFifty-six athletes (14–20 y) completed DATA and INTERVIEW in randomized counter-balanced order. OBSERVATION (n = 26) consisted of RDs recording participants’ food/drink intake in a 24-h period and were completed the day prior to DATA and INTERVIEW. Agreement among methods was estimated using a repeated measures t-test and Bland-Altman analysis.ResultsThe paired differences (with 95% confidence intervals) between DATA and OBSERVATION were not significant for carbohydrate (10.1%, -1.2–22.7%) and protein (14.1%, -3.2–34.5%) but was significant for energy (14.4%, 1.2–29.3%). There were no differences between DATA and INTERVIEW for energy (-1.1%, -9.1–7.7%), carbohydrate (0.2%, -7.1–8.0%) or protein (-2.7%, -11.3–6.7%). Bland-Altman analysis indicated significant positive correlations between absolute values of the differences and the means for OBSERVATION vs. DATA (r = 0.40 and r = 0.47 for energy and carbohydrate, respectively) and INTERVIEW vs. DATA (r = 0.52, r = 0.29, and r = 0.61 for energy, carbohydrate, and protein, respectively). There were also wide 95% limits of agreement (LOA) for most method comparisons. The mean bias ratio (with 95% LOA) for OBSERVATION vs. DATA was 0.874 (0.551-1.385) for energy, 0.906 (0.522-1.575) for carbohydrate, and 0.895(0.395-2.031) for protein. The mean bias ratio (with 95% LOA) for INTERVIEW vs. DATA was 1.016 (0.538-1.919) for energy, 0.995 (0.563-1.757) for carbohydrate, and 1.031 (0.514-2.068) for protein.ConclusionDATA has good relative validity for group-level comparisons in athletes. However, there are large variations in the relative validity of individuals’ dietary intake estimates from DATA, particularly in athletes with higher energy and nutrient intakes. DATA can be a useful athlete-specific, digital alternative to conventional 24-h dietary recall methods at the group level. Further development and testing is needed to improve DATA’s validity for estimations of individual dietary intakes.