The purpose of this study was to expand our previously published sweat normative data/analysis (n = 506) to establish sport-specific normative data for whole-body sweating rate (WBSR), sweat Na
+
, ...and rate of sweat Na
+
loss (RSSL). Data from 1303 athletes were compiled from observational testing (2000-2017) using a standardized absorbent sweat patch technique to determine local sweat Na
+
and normalized to whole-body sweat Na
+
. WBSR was determined from change in exercise body mass, corrected for food/fluid intake and urine/stool loss. RSSL was the product of sweat Na
+
and WBSR. There were significant differences between sports for WBSR, with highest losses in American football (1.51 ± 0.70 L/h), then endurance (1.28 ± 0.57 L/h), followed by basketball (0.95 ± 0.42 L/h), soccer (0.94 ± 0.38 L/h) and baseball (0.83 ± 0.34 L/h). For RSSL, American football (55.9 ± 36.8 mmol/h) and endurance (51.7 ± 27.8 mmol/h) were greater than soccer (34.6 ± 19.2 mmol/h), basketball (34.5 ± 21.2 mmol/h), and baseball (27.2 ± 14.7 mmol/h). After ANCOVA, significant between-sport differences in adjusted means for WBSR and RSSL remained. In summary, due to the significant sport-specific variation in WBSR and RSSL, American football and endurance have the greatest need for deliberate hydration strategies.
Abbreviations: WBSR: whole body sweating rate; SR: sweating rate; Na
+
: sodium; RSSL: rate of sweat sodium loss
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
2
) were 0.44–0.69 for RSR predicting WBSR best predictors: dorsal forearm ( r
2
= 0.62) and triceps ( r
2
= 0.69) and 0.55–0.77 for REG predicting WB sweat Na
+
best predictors: ventral forearm ( r
2
= 0.73) and thigh ( r
2
= 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.
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.
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 (
= 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.
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.
Published prediction equations improve the accuracy of estimating whole‐body sweat Na+ from regional measures, especially for the dorsal forearm, upper arm, chest, and upper back. Therefore, it is recommended that appropriate regression equations are applied when using the regional absorbent patch method to determine individualized sweat electrolyte losses.
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.
e12007
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 versus ion chromatography, HPLC) collected with regional absorbent patches during exercise in a hot‐humid environment. The HORIBA analyzers provided highly reliable test‐retest and day‐to‐day measurements of sweat Na+ and K+. The 95% limit of agreement between the SYRINGE HORIBA field technique and the reference laboratory‐based CENTRIFUGE HPLC technique was ±15.4 mEq/L and ±0.68 mEq/L for Na+ and K+, respectively; which may be acceptable in a field‐testing context, when simply aiming to estimate electrolyte losses for the purpose of identifying athletes/workers at greater risk for large electrolyte losses.
Purpose
To determine if tear fluid osmolarity (T
osm
) 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 (P
osm
) and T
osm
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) T
osm
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 T
osm
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 T
osm
tracked changes in hydration status similar to that of mean P
osm
; however, the individual responses in T
osm
to water restriction and water intake was considerably more variable than that of P
osm
.
Conclusion
T
osm
is a valid indicator of changes in hydration status when looking at the group mean; however, large differences among subjects in the T
osm
response to hydration changes limit its validity for individual recommendations.
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.
Abstract only
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, VO
2max
46.2±8.0 ml/kg/min) completed 90 min of cycling at 80±5% HR
max
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 cm
2
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 Information
This 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.
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