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+].

To assess the acute effects of two doses of coffeeberry extract use on (i) mental energy-related feelings (primary outcome) and (ii) cycling performance (secondary outcome). Twenty-eight active adults (14 females & 14 males: mean age = 20.6 ± 1.0 & 21.8 ± 3.8 years; VO2peak = 38.6 ± 5.2 & 44.7 ± 6.9 ml.kg.min−1) completed a randomized, double-blind, placebo-controlled, cross-over study. Treatments were a base beverage supplemented with a coffeeberry® extract (VDF FutureCeuticals, Inc.) at doses of 100 mg (CB100) and 300 mg (CB300). The base beverage alone was the placebo (PL) and the positive control was the base beverage with 75 mg caffeine (CAF). Participants consumed one of the four beverages during visits separated by at least five days. Before (BL) and one hour post-treatment, a battery of five cognitive tests and visual analog scales assessing the mood states of alertness, energy and fatigue were completed, taking 55-minutes. Two hours post-treatment, a 20-minute high intensity interval cycling protocol was performed followed by a 3-minute time trial; heart rate, ratings of perceived exertion, and feelings of fatigue were measured. Bonferroni corrected t-tests tested differences in cycling performance (P < .001). Repeated measures ANOVAs tested for other treatment effects. Mean differences from BL are presented below. There was a significant Beverage x Time interaction for alertness (P = .008), energy (P = .009), and fatigue (P = .008). Post-hoc analysis indicated from BL to POST CAF significantly improved alertness (PL: –2.2 ± 15.3; CB100: –.3 ± 11.2; CB300: 1.0 ± 11.7; CAF: 9.1 ± 12.5); energy (PL: –9.4 ± 54.7; CB100: –5.6 ± 36.4; CB300: 1.9 ± 35.9; CAF: 26.2 ± 40.8), and fatigue (PL: 11.8 ± 52.6; CB100: 6.5 ± 41.0; CB300: –.9 ± 43.9; CAF: –26.8 ± 42.9). Consumption of a beverage with 100 mg or 300 mg coffeeberry extract one hour before a cognitive test or two hours before a high intensity exercise bout does not influence feelings of alertness, energy, and fatigue or cycling performance. Consumption of a beverage with 75 mg caffeine had no impact on cycling performance but improved feelings of alertness, energy, and fatigue. PepsiCo R&D. The views expressed in this abstract are those of the authors and do not necessarily reflect the position or policy of PepsiCo, Inc.

Research has been equivocal on whether or not sweat lactate ([Lac]) and glucose ([Gluc]) are related to serum [Lac] and [Gluc]. PURPOSE: To determine the relationship between sweat [Lac] and [Gluc] versus serum [Lac] and [Gluc] during cycling exercise in the heat for 90 min. METHODS: Twelve moderately-trained recreational athletes (38 ± 6 y, 75.6 ± 14.5 kg, VO2max 45.6 ± 7.7 ml/kg/min) completed 90 min of cycling at 78 ± 5% HRmax in the heat (31°C, 50% RH). Prior to exercise, the forehead was cleaned with alcohol and deionized water, then three absorbent patches (10 cm2 absorbent pad, 3M Tegaderm™ + Pad) were applied sequentially (at 0, 30, and 60 min) and each patch was removed after 30-min increments of exercise alongside a synchronous blood draw. The forehead was re-cleaned with deionized water between each patch removal/application. Sweat and serum [Lac] and [Gluc] were measured using ion chromatography. Descriptive statistics were conducted across all collection time points for sweat and serum [Lac] and [Gluc]. Pearson’s product-moment correlations were performed to assess the relation between sweat and serum [Lac] and [Gluc] at the 90-min collection period. Due to limited sample volume the final n was 10 for each marker. Data are shown as mean ± SD. RESULTS: Forehead sweat [Lac] was 13.88 ± 3.29, 11.49 ± 3.13, and 11.91 ± 4.08 mM and serum [Lac] was 2.89 ± 1.33, 2.84 ± 0.64, 3.09 ± 1.13 mM, at 30, 60, and 90 min, respectively. Forehead sweat [Gluc] was 0.425 ± 0.417, 0.270 ± 0.239, and 0.357 ± 0.284 mg/dL and serum [Gluc] was 69 ± 15, 73 ± 16, 73 ± 13 mg/dL, at 30, 60, and 90 min, respectively. There was a moderate, but non-significant, positive correlation between sweat and serum [Lac], r(8) = 0.485, p = 0.155 and a minimal, but non-significant, positive correlation between sweat and serum [Gluc], r(8) = 0.186, p = 0.606. CONCLUSION: Sweat [Lac] and [Gluc] explain only 24% and 3% of the variation in serum [Lac] and [Gluc], respectively; suggesting other factors (aside from serum) impact sweat [Lac] and [Gluc]. Further research is warranted to understand the presence of lactate and glucose in the sweat and their applicability and relevance for use as biomarkers.

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.

Sweat testing is often conducted to assist with electrolyte replacement plans for athletes. However, the effect of patch application timing and on-skin duration on local sweating rate (LSR) and sweat electrolyte concentrations is unclear. PURPOSE: To determine the effect of patch application timing and on-skin duration on LSR and local sweat [Na+], [K+], and [Cl-]. METHODS: Thirty-nine recreationally trained (VO2max: 47.1±7.8 ml/kg/min) athletes (27 M, 12 F; 75.4±12.4 kg) cycled at ~80% HRmax in the heat (32°C, 39% rh). Prior to (PRE) and 15 min into exercise (EX), two sweat patches were applied to the left and right mid-back, respectively. The patches were removed after a skin adherence duration of 30 (SHORT) or 70 (LONG) min. LSR was equated from sweat mass over patch surface area (11.9 cm2) and duration. Sweat was centrifuged and analyzed for [Na+], [K+], and [Cl-] by ion chromatography. A two-way repeated measures ANOVA was used to determine the effect of patch application timing (PRE vs. EX), duration (SHORT vs. LONG), and interaction effects on each variable, followed by Tukey post-hoc where main effects were found. Significance was set at p<0.05. RESULTS: There was an interaction effect with EX LONG and EX SHORT > PRE LONG > PRE SHORT for [Na+] (56.8±21.6, 58.5±22.3 > 50.7±20.1 > 46.8±19.6 mmol/L, p<0.0001), [Cl-] (55.2±23.5, 53.5±25.1 > 49.4±22.1 > 38.2±21.7 mmol/L, p<0.0001), and LSR (1.4±0.3, 1.6±0.6 > 1.1±0.3 > 0.8±0.4 mg/cm2/min, p<0.0001). There were no significant differences for [K+] between EX LONG, EX SHORT, PRE LONG, AND PRE SHORT (3.8±0.6, 4.0±0.9, 3.6±0.6, 3.3±0.6 mmol/L, p=0.79). CONCLUSION: The on-skin duration did not affect sweat [Na+] and [Cl-] when patches were applied during exercise. However, applying patches prior to exercise resulted in lower sweat [Na+] and [Cl-], especially when removed after a short duration. This was likely due to lower LSR during the ramp up to steady state sweating. Therefore, practitioners should take patch application timing into account when interpreting sweat electrolyte results. Local sweat [Na+] and [Cl-] measured from patches applied prior to exercise may not be representative of concentrations during the full bout of exercise. However, more research is needed to determine the impact of patch timing in the context of whole body sweat [Na+] and [Cl-] estimations.

We have previously published regression 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. PURPOSE: To determine the validity of published regression equations (Baker et al. 2018) in predicting WB sweat [Na] from REG measures when applied to a new data set. METHODS: Forty-nine recreational athletes (34 men, 15 women; 75±12 kg) cycled for 90 min while WB sweat [Na] was measured using the washdown technique. Exercise intensity (82% HRmax) and environmental conditions (32°C, 39% rh, 2.4 m/s air flow) were similar to the 2018 study in which the prediction equations were developed. REG sweat [Na] was measured from seven regions using absorbent patches (3M Tegaderm+Pad, 10 cm). Regression equations from Baker et al. 2018 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]. RESULTS: Mean±SD WB sweating rate was 0.94±0.32 L/h and measured WB sweat [Na] was 41±16 mmol/L. 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. CONCLUSIONS: The use of regression equations enables prediction of WB sweat [Na] within a mean bias of 0-5 mmol/L and within a 95% LOA of ±12-17 mmol/L across all sites. By contrast, the use of raw REG [Na] increases the mean bias to 9-23 mmol/L for the dorsal forearm, upper arm, chest, and upper back, and increases the 95% LOA to ±15-25 mmol/L across all sites. Regression equations improve the accuracy of estimating WB sweat [Na] from REG measures and are therefore recommended for use in Na balance studies and field tests to determine individualized sweat electrolyte losses.

Wearable microfluidic system enables remote analysis of sweat rate and chloride loss for assessing personalized fluid needs.

Research has been limited and mixed with regards to the effect of normal, short-term dietary sodium (Na) intake on sweat Na concentration ([Na]) and total sweat Na losses during exercise. PURPOSE: To determine the relation between dietary Na intake during exercise and up to 48-h before exercise on whole-body (WB) sweat [Na] and total sweat Na loss during 90 min of moderate-intensity cycling in the heat. METHODS: Forty-nine recreational athletes (34 men, 15 women; 34 ± 4 years; 75 ± 12 kg) cycled for 90 min at 78 ± 5% HRmax in the heat (320C, 25-50% RH). The WB washdown technique was used to collect sweat electrolytes during exercise and ion chromatography analysis was used to determine sweat [Na]. Total sweat Na loss was the product of WB sweat [Na] and WB sweat loss. Subjects were instructed to consume their normal diet before their trials. Upon arriving to the lab, each subject turned in a diet log, which included specific portion sizes and brand/type for all foods, fluids, and dietary supplements consumed in the previous 48 h. The investigators reviewed the diet logs for completeness with the subjects. Na intake was determined by Registered Dietitians using a computer based dietary analysis tool. Na intake during the trial was determined from the volume of 6% carbohydrate electrolyte (38 mmol/L Na) solution consumed ad libitum. Pearson correlation analysis was used to determine the relation between Na intake versus WB sweat [Na] and total sweat Na loss. Data are shown as mean ± SD. RESULTS: WB sweat [Na] was 41.1 ± 15.6 mmol/L and total sweat Na losses were 60.9 ± 35.3 mmol. Na intake during exercise, 24-h and 48-h before exercise were 32.4 ± 18.0 mmol, 188 ± 102 mmol, and 350 ± 159 mmol. There were no significant correlations between dietary Na intake and WB sweat [Na] (r = -0.002 to 0.02, p=0.90-0.99) or total sweat Na losses (r = 0.07 to 0.19, p=0.20-0.61) for any of the comparisons. CONCLUSION: There were no correlations between normal dietary Na intake during and up to 48 h before exercise versus WB sweat [Na] or total sweat Na losses. These results suggest that short-term Na intake does not play a significant role in explaining the inter-individual differences in WB sweat Na during exercise.

Interleukin (IL) 1 cytokines, IL‐1α and IL‐1β, are well known for their immunological responses as blood biomarkers; however, it is unclear if sweat cytokine concentrations can be used to predict that of blood. The purpose of this study was to determine the correlations between sweat and serum for IL‐1α and IL‐1β concentrations measured during exercise. Nine moderately‐trained recreational athletes (35±7 y, 73.9±14.4 kg, VO2max 46.2±8.0 ml/kg/min) completed 90 min of cycling at 80±5% HRmax in the heat (31°C, 50% RH). Preceding exercise, left ventral forearm (LVF) was cleaned with alcohol and deionized water, and an absorbent patch (10 cm2 absorbent pad, 3M Tegaderm™ + Pad) was applied 10 min into exercise. The patch was removed at the end of 90 min of exercise alongside a synchronous blood draw. Sweat and serum IL‐1α and IL‐1β were measured using Multiplex (EMD Millipore, MagPix) IL‐1α and IL‐1β kits. Spearman correlation was performed to determine the relation between sweat and serum for IL‐1α and IL‐1β. Non parametric sign test was used to determine mean differences between sweat and serum. Cytokine measurements are shown as mean ± SD. There were no significant correlations between sweat and serum for IL‐1α (r=−0.19, p=0.61) or IL‐1β (r=0.20, p=0.61). There were significant mean differences in IL‐1α expression between sweat (1004.5±613.8 pg/mL) and serum (24.9±58.1 pg/mL) (p=0.004), and non‐significant mean differences in IL‐1β between sweat (2.3±1.9 pg/mL) and serum (1.3±1.3 pg/mL) (p=0.45). These results suggest that there were no significant correlations between sweat and serum IL‐1 cytokine concentrations. Unlike IL‐1β, IL‐1α expression in sweat was significantly different between sweat and serum. Therefore, it seems that IL‐1 expression in sweat may have limited utility in predicting blood IL‐1 concentrations.Support or Funding InformationThis study was funded by the Gatorade Sports Science Institute, a division of PepsiCo, Inc. The views expressed in this abstract are those of the authors and do not necessarily reflect the position or policy of PepsiCo, Inc.

Assessing regional sweat electrolyte concentrations using standard patch techniques requires post-collection benchtop harvesting and analysis of sweat, which precludes real-time feedback to athletes. A technique enabling on-skin analysis is needed to advance the practicality of sweat testing. PURPOSE: To determine the accuracy and reliability of a novel epidermal microfluidic patch with built-in colorimetric assay (Epifluidic patch) to measure regional sweat [Cl-]. METHODS: Twenty-three subjects (15 male, 8 female; 18-42 y; 72.3±11.2 kg) cycled at 85% HRmax in a warm laboratory (30°C, 50% rh) while sweat was collected from the right and left ventral forearms with an Absorbent patch (3M Tegaderm+Pad) and Epifluidic patch (Epicore Biosystems, Inc.), respectively. A subset of subjects (n=9) completed two identical trials 2-4 days apart to determine test-retest reliability. Immediately after removal of the Absorbent patch, an image was taken of the Epifluidic patch on-skin with a digital single-lens reflex camera for analysis of [Cl-] via colorimetry. Sweat from the Absorbent patch was extracted via centrifuge and subsequently analyzed for [Cl-] by ion chromatography. Data are shown as mean±SD. RESULTS: There was no difference in sweat [Cl-] between Absorbent and Epifluidic patches (32.9±16.8 vs. 34.5±19.6 mmol/L, p=0.21). Bland-Altman Limits of Agreement between methods was -10.1 to 13.3 mmol/L. There was a significant correlation between patches (r=0.96, p<0.0001) and the coefficient of determination (r2) for predicting Absorbent from Epifluidic patch [Cl-] was 0.92. Based on Deming regression analysis, the slope and intercept of the regression line describing Absorbent vs. Epifluidic patch sweat [Cl-] were not different than 1 and 0, respectively. Sweat [Cl-] was not different between repeat trials for the Absorbent (1.4±4.4 mmol/L, p=0.36) or Epifluidic patch (-0.4±1.6 mmol/L, p=0.51) and test-retest CVs were 12% and 4%, respectively. CONCLUSIONS: The Epifluidic patch provides accurate and reliable data for forearm sweat [Cl-] estimation during exercise in controlled laboratory conditions. Future research is needed to evaluate the Epifluidic Colorimetric Patch for on-skin analysis of sweat [Cl-] at other regional sites as well as during live practices and games.

Various skin preparation methods, ranging from MINIMAL (alcohol wipes) to more THOROUGH (e.g., shaving and cleaning), have been used to remove surface contamination prior to patch application for sweat electrolyte measurements. Using MINIMAL cleaning methods could improve athlete participation and the efficiency of sweat testing in the field, but it is unknown if this would result in higher sweat [Na+], [Cl-], and [K+] due to insufficient removal of surface contamination. PURPOSE: To compare the effect of MINIMAL vs. THOROUGH skin cleaning methods on regional sweat [Na+], [Cl-], and [K+]. METHODS: Thirteen subjects (7 male, 6 female; 23-45 y; 74.6±15.8 kg) cycled at ~80% HRmax in a warm laboratory (30°C, 50% rh) while sweat was collected from right (RDF) and left (LDF) dorsal forearms with absorbent patches (3M™ Tegaderm+Pad). Prior to patch application (20 min before exercise), the RDF was shaved, cleaned with alcohol, wiped with deionized water, and dried with gauze (THOROUGH). The LDF was cleaned with alcohol wipes only (MINIMAL). Patches were removed upon adequate sweat absorption (0.60±0.15 g, 57±15 min). Sweat from absorbent patches was extracted via centrifuge and subsequently analyzed for [Na+], [Cl-], and [K+] by ion chromatography. Regional sweating rate (RSR) was determined via gravimetry. RESULTS: There were no differences between MINIMAL and THOROUGH for sweat [Na+] (54.4±24.7 vs. 53.4±23.8 mM, p=0.06) or sweat [Cl-] (45.2±23.2 vs. 44.4±22.2 mM, p=0.13). Bland Altman 95% limits of agreement (LOA) were 4.0 to -2.2 mM and 4.8 to -3.0 mM for sweat [Na+] and [Cl-], respectively. Sweat [K+] was higher with MINIMAL vs. THOROUGH cleaning (5.0±0.8 vs. 4.5±0.6 mM, p=0.001; LOA: 1.3 to -0.3 mM). RSR was not different between cleaning methods (0.973±0.411 vs. 0.954±0.406 mg/cm2/min, p=0.75; LOA: 0.435 to -0.397 mg/cm2/min). CONCLUSIONS: MINIMAL cleaning of the skin with alcohol results in similar regional sweat [Na+] and [Cl-] compared with more THOROUGH preparation that includes shaving of hair and cleaning with alcohol and deionized water. Sweat [K+] is statistically (but not practically) higher when MINIMAL cleaning is conducted. THOROUGH skin preparation prior to sweat testing may not be warranted; although future research in field conditions is needed to confirm that MINIMAL cleaning is adequate.

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.

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.

Previous research has measured the amount of sweat absorbed in basketball uniforms during exercise, but data are limited in other sports. PURPOSE: To determine the amount of trapped sweat (TS) in various sports uniforms during sport-specific, laboratory-based exercise. METHODS: Eleven male (30 ± 5 years, 75.7 ± 5.2 kg) and 6 female (29 ± 4 years, 59.9 ± 9.9 kg) moderately-trained athletes completed 3 trials consisting of 120 min intermittent sport-specific exercise in standard uniforms for various sports, including football (n=9 men), basketball (n= 4 men, 5 women), soccer (n=4 men, 5 women), baseball/softball (n=4 men, 4 women), and/or endurance (n=5 men, 4 women) in a temperature- controlled laboratory (basketball: 25°C, 55% rh; all other sports: 30°C, 55% rh). Protocols were designed to simulate the demands of each sport (endurance: 82 ± 5% HRmax, RPE 13 ± 2; football: 75 ± 10% HRmax, RPE 13 ± 1; soccer: 77 ± 10% HRmax, RPE 12 ± 1; basketball: 66 ± 12% HRmax, RPE 10 ± 2; and baseball/softball: 59 ± 3% HRmax, RPE 9 ± 2). Sweat loss (SL) was determined from change in nude body mass corrected for fluid intake, urine loss, respiratory water loss, and metabolic mass loss. Nude and clothed body mass were measured pre- and post-exercise to determine TS. Analysis of variance followed by Tukey’s post hoc test was used to compare sports. Data are mean ± SD. RESULTS: There were significant differences in SL between sports (p<0.0001): football (2.61 ± 0.36 kg), endurance (2.18 ± 0.53 kg) and soccer (1.99 ± 0.81 kg) > basketball (1.24 ± 0.37 kg) and baseball/softball (1.19 ± 0.38 kg). There were also significant differences in TS (p<0.0001): football (0.58 ± 0.14 kg) > endurance (0.28 ± 0.16 kg) and soccer (0.24 ±0.18 kg) > basketball (0.11 ± 0.08 kg) and baseball/softball (0.15 ± 0.12 kg). TS as a percentage of SL was significantly (p<0.0001) higher in football (22.5 ± 3.8%) than endurance (12.2 ± 4.7%), soccer (10.9 ± 3.4%), basketball (9.2 ± 4.4%), and baseball/softball (10.8 ± 6.2%). CONCLUSION: Sports with higher SL were associated with higher volumes of TS in uniforms. The football uniform (including full pads) led to the most TS and greatest underestimations in SL. Such high volumes of TS are also likely to have ramifications for evaporative heat loss capacity and therefore warrant future research investigating the effects of TS on thermoregulation.

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.

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.

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.

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<Moderate<High, ANOVA). During Low, subjects lost 1.10 ± 0.59 kg sweat and TS was 0.11 ± 0.15 kg (8.0 ± 5.1% SL). During Moderate, subjects lost 1.60 ± 0.56 kg sweat and TS was 0.21 ± 0.21 kg (11.6 ± 6.3% SL). During High, subjects lost 2.12 ± 0.66 kg sweat and TS was 0.38 ± 0.28 kg (16.0 ± 7.4% SL). Multiple regression and partial correlation analysis suggested TS was significantly related to SL (P < 0.0001; partial r = 0.81-0.89), whereas the contributions of body mass (P = 0.22-0.92) and age (P = 0.29-0.44) were not significant. TS during on-court training was 0.35 ± 0.36 kg, which was associated with a 14.1 ± 11.5% underestimation in SL, and was not statistically different than laboratory-based results (P = 0.59). Clothed body mass measurements should be used with caution, as TS is highly variable and can cause a significant underestimation in SL in athletes with high sweating rates.

The absorbent patch method is often used to estimate whole body (WB) sweating responses in athletes. However, no study havs investigated the relation between results obtained with the local absorbent patch method (L) versus WB for both sweating rate (SR) and sweat sodium concentration ([Na+]). Therefore, the objective of this study was to determine the relation between LSR and WBSR as well as local and WB sweat [Na+] during moderate intensity exercise. Thirteen non‐heat acclimated, male, recreational athletes (age: 31 ± 6 y; BSA: 1.88 ± 0.18 m2; VO2max: 51±8 ml/kg/min) completed 90 min of moderate intensity cycling (75–85% maximal heart rate) in a warm environment (30°C, 41% relative humidity) to determine WB sweat [Na+] using the washdown technique. In addition, small sweat samples were collected from the dorsal and ventral forearm, tricep, chest, scapula, lower back, ventral thigh, calf, and forehead with absorbent patches (3M TegadermTM+Pad; pad size 10 cm2) and analyzed via ion chromatography to determine local sweat [Na+]. WBSR was determined from body mass change over time corrected for fluid intake, urine output, respiratory water loss, and metabolic mass loss. LSR was determined from the mass change in the absorbent pad and the duration of pad time on the skin. A linear regression analysis and Pearson product moment correlation were used to determine the relation between LSR versus WBSR and local versus WB sweat [Na+]. A repeated‐measures one‐way ANOVA with Dunnett post hoc test was used to determine differences between LSR and WBSR as well as local and WB sweat [Na+]. Data are shown as mean ± SD. WBSR and WB sweat [Na+] were 0.63 ± 19 mg/cm2/min and 40 ± 14 mmol/L, respectively. LSR and local sweat [Na+] from all nine anatomical sites were significantly correlated with WBSR (r2: 0.43–0.71, p < 0.05) and WB sweat [Na+] (r2: 0.59–0.82, p < 0.01), respectively. There was no difference between calf LSR (0.73 ± 31 mg/cm2/min) and WBSR. For all other anatomical sites LSR was significantly greater than WBSR (by +0.34 ± 0.25 mg/cm2/min (thigh) to +3.86 ± 2.86 mg/cm2/min (forehead), p < 0.001). With respect to sweat [Na+], ventral forearm, dorsal forearm, thigh, calf, and lower back were not different than WB (36 ± 20 mmol/L (calf) to 50 ± 24 mmol/L (lower back)). However, local sweat [Na+] from the scapula, tricep, forehead, and chest were significantly greater than WB sweat [Na+] (by +12 ± 11 mmol/L (tricep) to +31 ± 16 mmol/L (scapula), p < 0.001). In conclusion, while many local sites overestimated WB sweating responses, there were significant correlations between LSR and WBSR as well as local and WB sweat [Na+] at all nine anatomical sites tested. In general, it seems that the limbs (e.g., dorsal and ventral forearms, ventral thigh, and calf) are the most representative of WBSR and WB sweat [Na+]. These results can help inform sweat testing best practices in non‐heat acclimated, male, recreational athletes during moderate exercise in a warm environment.Support or Funding InformationThis study was funded by the Gatorade Sports Science Institute, a division of PepsiCo, Inc. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of PepsiCo, Inc.

Few studies have conducted research to develop lab-based protocols to simulate physiological and subjective exertion during team sports. Therefore, we developed an intermittent protocol to simulate the demands of basketball training; 3 levels of protocol intensity were designed to include the full range of practice types (e.g., walk-through to scrimmage/conditioning). PURPOSE: To determine whether this protocol achieves 3 distinct progressive levels of intensity based on various physiological and subjective measures of exertion. METHODS: 21 male basketball players (24 ± 10 y, 77.2 ± 13.5 kg) completed 3 randomized trials [low (L), moderate (M), and high-intensity (H)] consisting of 5 X 30-min bouts of intermittent exercise in a temperate room (23 °C, 62% rh). The L, M, and H trials differed in the percentage of time running (9 mph), sprinting (12 mph), and performing drills (lateral slides, jumping, footwork drills, pushups, basketball passes) (L: 11%, M: 26%, and H: 45%) and time standing, walking (3.5 mph), and jogging (7 mph) (L: 89%, M: 74%, and H: 55%). Heart rate (Polar) and RPE (Borg 6-20 scale) were recorded every 5 min (reported as mean ± SD). Before and after exercise subjects rated their muscle fatigue, tiredness, effort, and physical demand on a 100-point visual analog scale (VAS, reported as mean change ± SD). Repeated measures ANOVA with Bonferroni correction was used to determine the effect of exercise intensity on HR, RPE, and VAS ratings. RESULTS: Exercise intensity had a significant effect on HR (L: 63 ± 6, M: 75 ± 6, and H: 82 ± 6% HRmax), RPE (L: 9 ± 1, M: 12 ± 1, 14 ± 1), muscle fatigue (L: 18 ± 3, M: 41 ± 5, H: 62 ± 4), tiredness (L: 10 ± 4, M: 34 ± 6, H: 67 ± 4), effort (L: 21 ± 3, M: 46 ± 3, H: 72 ± 4), and physical demand (L: 21 ± 4, M: 44 ± 4, H: 67 ± 5), where L < M < H (all p values were < 0.01). CONCLUSION: Our lab-based, basketball-simulated protocol achieved 3 distinct progressive levels of intensity, based on HR, RPE, and ratings of muscle fatigue, tiredness, effort and physical demand. The HR and RPE observed during H were similar to that reported during live basketball scrimmages/games (~80-90% and ~14, respectively). Further research is needed to determine the ecological validity and repeatability of our basketball protocol and whether these measures could be used in a predictive model for the assessment of on-court training intensity.