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.

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.

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.

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.