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.

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.

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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.

Basketball players face multiple challenges to in-season recovery. The purpose of this article is to review the literature on recovery modalities and nutritional strategies for basketball players and practical applications that can be incorporated throughout the season at various levels of competition. Sleep, protein, carbohydrate, and fluids should be the foundational components emphasized throughout the season for home and away games to promote recovery. Travel, whether by air or bus, poses nutritional and sleep challenges, therefore teams should be strategic about packing snacks and fluid options while on the road. Practitioners should also plan for meals at hotels and during air travel for their players. Basketball players should aim for a minimum of 8 h of sleep per night and be encouraged to get extra sleep during congested schedules since back-to back games, high workloads, and travel may negatively influence night-time sleep. Regular sleep monitoring, education, and feedback may aid in optimizing sleep in basketball players. In addition, incorporating consistent training times may be beneficial to reduce bed and wake time variability. Hydrotherapy, compression garments, and massage may also provide an effective recovery modality to incorporate post-competition. Future research, however, is warranted to understand the influence these modalities have on enhancing recovery in basketball players. Overall, a strategic well-rounded approach, encompassing both nutrition and recovery modality strategies, should be carefully considered and implemented with teams to support basketball players’ recovery for training and competition throughout the season.