Explaining variation in sweat sodium concentration: effect of individual characteristics and exercise, environmental, and dietary factors.

This study determined the relative importance of several individual characteristics and dietary, environmental, and exercise factors in determining sweat [Na+] during exercise. Data from 1944 sweat tests were compiled for a retrospective analysis. Stepwise multiple regression (P < 0.05 threshold for inclusion) and T values were used to express the relative importance of each factor in a model. Three separate models were developed based on available independent variables: model 1 (1,944 sweat tests from 1,304 subjects); model 2 (subset with energy expenditure: 1,003 sweat tests from 607 subjects); model 3 (subset with energy expenditure, dietary sodium, and V̇o2max: n = 48). Whole body sweat [Na+] was predicted from forearm sweat patches in models 1 and 2 and directly measured using whole body washdown in model 3. There were no significant effects of age group, race/ethnicity, relative humidity, exercise duration, pre-exercise urine specific gravity, exercise fluid balance, or dietary or exercise sodium intake on any model. Significant predictors in model 1 (adjusted r2 = 0.17, P < 0.001) were season of the year (warm, T = -6.8), exercise mode (cycling, T = 6.8), sex (male, T = 4.9), whole body sweating rate (T = 4.5), and body mass (T = -3.0). Significant predictors in model 2 (adjusted r2 = 0.19, P < 0.001) were season of the year (warm, T = -5.2), energy expenditure (T = 4.7), exercise mode (cycling, T = 3.6), air temperature (T = 3.0), and sex (male, T = 2.7). The only significant predictor in model 3 (r2 = 0.23, P < 0.001) was energy expenditure (T = 3.8). In summary, the models accounted for 17%-23% of the variation in whole body sweat [Na+] and energy expenditure and season of the year (proxy for heat acclimatization) were the most important factors.NEW & NOTEWORTHY This comprehensive analysis of a large, diverse data set contributes to our overall understanding of the factors that influence whole body sweat [Na+]. The main finding was that energy expenditure was directly associated with whole body sweat [Na+], potentially via the relation between energy expenditure and whole body sweating rate (WBSR). Warmer months (proxy for heat acclimatization) were associated with lower whole body sweat [Na+]. Exercise mode, air temperature, and sex may also have small effects, but other variables (age group, race/ethnicity, fluid balance, sodium intake, relative V̇o2max) had no association with whole body sweat [Na+]. Taken together, the models explained 17%-23% of the variation in whole body sweat [Na+].

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.

Cross-validation of equations to predict whole-body sweat sodium concentration from regional measures during exercise.

We have previously published equations to estimate whole-body (WB) sweat sodium concentration ([Na+ ]) from regional (REG) measures; however, a cross-validation is needed to corroborate the applicability of these prediction equations between studies. The purpose of this study was to determine the validity of published equations in predicting WB sweat [Na+ ] from REG measures when applied to a new data set. Forty-nine participants (34 men, 15 women; 75 ± 12 kg) cycled for 90 min while WB sweat [Na+ ] was measured using the washdown technique. REG sweat [Na+ ] was measured from seven regions using absorbent patches (3M Tegaderm + Pad). Published equations were applied to REG sweat [Na+ ] to determine predicted WB sweat [Na+ ]. Bland-Altman analysis of mean bias (raw and predicted minus measured) and 95% limits of agreement (LOA) were used to compare raw (uncorrected) REG sweat [Na+ ] and predicted WB sweat [Na+ ] to measured WB sweat [Na+ ]. Mean bias (±95% LOA) between raw REG sweat [Na+ ] and measured WB sweat [Na+ ] was 10(±20), 0(±19), 9(±20), 22(±25), 23(±24), 0(±15), -4(±18) mmol/L for the dorsal forearm, ventral forearm, upper arm, chest, upper back, thigh, and calf, respectively. The mean bias (±95% LOA) between predicted WB sweat [Na+ ] and measured WB sweat [Na+ ] was 3(±14), 4(±12), 0(±14), 2(±17), -2(±16), 5(±13), 4(±15) mmol/L for the dorsal forearm, ventral forearm, upper arm, chest, upper back, thigh, and calf, respectively. Prediction equations improve the accuracy of estimating WB sweat [Na+ ] from REG and are therefore recommended for use when determining individualized sweat electrolyte losses.

Exercise intensity effects on total sweat electrolyte losses and regional vs. whole-body sweat [Na+], [Cl-], and [K+].

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 VO2max 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% VO2max), but other sites (especially ventral wrist, lower back, thigh, and calf) require separate prediction equations accounting for workload.

Body map of regional vs. whole body sweating rate and sweat electrolyte concentrations in men and women during moderate exercise-heat stress.

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 ( r2) were 0.44-0.69 for RSR predicting WBSR [best predictors: dorsal forearm ( r2 = 0.62) and triceps ( r2 = 0.69)] and 0.55-0.77 for REG predicting WB sweat [Na+] [best predictors: ventral forearm ( r2 = 0.73) and thigh ( r2 = 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.

Sweat Sodium, Potassium, and Chloride Concentrations Analyzed Same Day as Collection Versus After 7 Days Storage in a Range of Temperatures.

The purpose of this study was to determine the effect of storage temperature on sodium ([Na+]), potassium ([K+]), and chloride ([Cl-]) concentrations of sweat samples analyzed 7 days after collection. Using the absorbent patch technique, 845 sweat samples were collected from 39 subjects (32 ± 7 years, 72.9 ± 10.5 kg) during exercise. On the same day as collection (PRESTORAGE), 609 samples were analyzed for [Na+], [Cl-], and [K+] by ion chromatography (IC) and 236 samples were analyzed for [Na+] using a compact ion-selective electrode (ISE). Samples were stored at one of the four conditions: -20 °C (IC, n = 138; ISE, n = 60), 8 °C (IC, n = 144; ISE, n = 59), 23 °C (IC, n = 159; ISE, n = 59), or alternating between 8 °C and 23 °C (IC, n = 168; ISE, n = 58). After 7 days in storage (POSTSTORAGE), samples were reanalyzed using the same technique as PRESTORAGE. PRESTORAGE sweat electrolyte concentrations were highly related to that of POSTSTORAGE (intraclass correlation coefficient: .945-.989, p < .001). Mean differences (95% confidence intervals) between PRESTORAGE and POSTSTORAGE were statistically, but not practically, significant for most comparisons: IC [Na+]: -0.5(0.9) to -2.1(0.9) mmol/L; IC [K+]: -0.1(0.1) to -0.2(0.1) mmol/L; IC [Cl-]: -0.4(1.4) to -1.3(1.3) mmol/L; ISE [Na+]: -2.0(1.1) to 1.3(1.1) mmol/L. Based on typical error of measurement results, 95% of the time PRESTORAGE and POSTSTORAGE sweat [Na+], [K+], and [Cl-] by IC analysis fell within ±7-9, ±0.6-0.7, and ±9-13 mmol/L, respectively, while sweat [Na+] by ISE was ±6 mmol/L. All conditions produced high reliability and acceptable levels of agreement in electrolyte concentrations of sweat samples analyzed on the day of collection versus after 7 days in storage.

Trapped sweat in basketball uniforms and the effect on sweat loss estimates.

The aims of this study were to determine: (1) trapped sweat (TS) in basketball uniforms and the effect on sweat loss (SL) estimates during a laboratory-based basketball simulation protocol; (2) the impact of exercise intensity, body mass, age, and SL on TS; and (3) TS during on-court training to assess the ecological validity of the laboratory-based results. Twenty-four recreational/competitive male basketball players (23 ± 10 years, 77.0 ± 16.7 kg) completed three randomized laboratory-based trials (Low, Moderate, and High intensity) consisting of 150-min intermittent exercise. Eighteen elite male players (23 ± 4 years, 92.0 ± 20.6 kg) were observed during coach-led, on-court training. Nude and clothed body mass were measured pre and postexercise to determine TS. Data are mean ± SD. There was a significant effect of intensity on SL and TS (P < 0.001, Low

Selected In-Season Nutritional Strategies to Enhance Recovery for Team Sport Athletes: A Practical Overview.

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.

Fluid Balance in Team Sport Athletes and the Effect of Hypohydration on Cognitive, Technical, and Physical Performance.

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.

Non-invasive estimation of hydration status changes through tear fluid osmolarity during exercise and post-exercise rehydration.

PURPOSE: To determine if tear fluid osmolarity (Tosm) can track changes in hydration status during exercise and post-exercise rehydration. METHODS: Nineteen male athletes (18-37 years, 74.6 ± 7.9 kg) completed two randomized, counterbalanced trials; cycling (~95 min) with water intake to replace fluid losses or water restriction to progressively dehydrate to 3 % body mass loss (BML). After exercise, subjects drank water to maintain body mass (water intake trials) or progressively rehydrate to pre-exercise body mass (water restriction trials) over a 90-min recovery period. Plasma osmolality (Posm) and Tosm measurements (mean of right and left eyes) were taken pre-exercise, during rest periods between exercise bouts corresponding to 1, 2, and 3 % BML, and rehydration at 2, 1, and 0 % BML. RESULTS: During exercise mean (± SD) Tosm was significantly higher in water restriction vs. water intake trials at 1 % BML (299 ± 9 vs. 293 ± 9 mmol/L), 2 % BML (301 ± 9 vs. 294 ± 9 mmol/L), and 3 % BML (302 ± 9 vs. 292 ± 8 mmol/L). Mean Tosm progressively decreased during post-exercise rehydration and was not different between trials at 1 % BML (291 ± 8 vs. 290 ± 7 mmol/L) and 0 % BML (288 ± 7 vs. 289 ± 8 mmol/L). Mean Tosm tracked changes in hydration status similar to that of mean Posm; however, the individual responses in Tosm to water restriction and water intake was considerably more variable than that of Posm. CONCLUSION: Tosm is a valid indicator of changes in hydration status when looking at the group mean; however, large differences among subjects in the Tosm response to hydration changes limit its validity for individual recommendations.

Acute effects of dietary constituents on motor skill and cognitive performance in athletes.

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.

Validity and reliability of a field technique for sweat Na+ and K+ analysis during exercise in a hot-humid environment.

Abstract 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.

Validity and relative validity of a novel digital approach for 24-h dietary recall in athletes.

BACKGROUND: We 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. METHODS: Fifty-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. RESULTS: The 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. CONCLUSION: DATA 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.

Dietitian-observed macronutrient intakes of young skill and team-sport athletes: adequacy of pre, during, and postexercise nutrition.

CONTEXT: Sports nutrition experts recommend that team-sport athletes participating in intermittent high-intensity exercise for ≥1 hr consume 1-4 g carbohydrate/kg 1-4 hr before, 30-60 g carbohydrate/hr during, and 1-1.2 g carbohydrate/kg/hr and 20-25 g protein as soon as possible after exercise. The study objective was to compare observed vs. recommended macronutrient intake of competitive athletes under free-living conditions. METHODS: The dietary intake of 29 skill/team-sport athletes (14-19 y; 22 male, 7 female) was observed at a sports training facility by trained registered dietitians for one 24-hr period. Dietitians accompanied subjects to the cafeteria and field/court to record their food and fluid intake during meals and practices/competitions. Other dietary intake within the 24-hr period (e.g., snacks during class) was accounted for by having the subject take a picture of the food/fluid and completing a log. RESULTS: For male and female athletes, respectively, the mean ± SD (and percent of athletes meeting recommended) macronutrient intake around exercise was 1.4 ± 0.6 (73%) and 1.4 ± 1.0 (57%) g carbohydrate/kg in the 4 hr before exercise, 21.1 ± 17.2 (18%) and 18.6 ± 13.2 (29%) g carbohydrate/hrr during exercise, 1.4±1.1 (68%) and 0.9± 1.0 (43%) g carbohydrate/kg and 45.2 ± 36.9 (73%) and 18.0 ± 21.2 (43%) g protein in the 1 hr after exercise. CONCLUSION: The male athletes' carbohydrate and protein intake more closely approximated recommendations overall than that of the female athletes. The most common shortfall was carbohydrate intake during exercise, as only 18% of male and 29% of female athletes consumed 3060 g carbohydrate/hr during practice/competition.