Regional and time course differences in sweat cortisol, glucose, and select cytokine concentrations during exercise.

INTRODUCTION: The use of sweat as a biofluid for non-invasive sampling and diagnostics is a popular area of research. However, concentrations of cortisol, glucose, and cytokines have not been described across anatomical regions or as time progresses throughout exercise. PURPOSE: To determine regional and time course differences in sweat cortisol, glucose, and select cytokines (EGF, IFN-γ, IL-1ß, IL-1α, IL-1ra, TNF-α, IL-6, IL-8, and IL-10). METHODS: Sweat was collected with absorbent patches from eight subjects (24-44 y; 80.2 ± 10.2 kg) on the forehead (FH), right dorsal forearm (RDF), right scapula (RS), and right triceps (RT) at 0-25 min, 30-55 min, and 60-85 min during 90 min of cycling (~ 82% HRmax) in a heated chamber (32 °C, 50% rh). ANOVA was used to determine the effect of site and time on outcomes. Data are reported as LS means ± SE. RESULTS: There was a significant effect of location on sweat analyte concentrations with FH having higher values than most other regions for cortisol (FH: 1.15 ± 0.08 ng/mL > RDF: 0.62 ± 0.09 ng/mL and RT: 0.65 ± 0.12 ng/mL, P = 0.02), IL-1ra (P < 0.0001), and IL-8 (P < 0.0001), but lower concentrations for glucose (P = 0.01), IL-1α (P < 0.0001), and IL-10 (P = 0.02). Sweat IL-1ß concentration was higher on the RS than RT (P < 0.0001). Sweat cortisol concentration increased (25 min: 0.34 ± 0.10 ng/mL < 55 min: 0.89 ± 0.07 ng/mL < 85 min: 1.27 ± 0.07 ng/mL; P < 0.0001), while EGF (P < 0.0001), IL-1ra (P < 0.0001), and IL-6 (P = 0.02) concentrations decreased over time. CONCLUSION: Sweat analyte concentrations varied with time of sampling and anatomical region, which is essential information to consider when conducting future work in this area. CLINICAL TRIAL IDENTIFIER: NCT04240951 registered January 27, 2020.

Multiple regression analyses to determine the effect of sweating rate and tattoo characteristics on sweat outcome measures during exercise.

PURPOSE: To compare local sweating rate (LSR) and local sweat sodium ([Na+]), chloride ([Cl-]), and potassium ([K+]) concentrations of tattooed skin and contralateral non-tattooed skin during exercise. METHODS: Thirty-three recreational exercisers (17 men, 16 women) with ≥ 1 unilateral permanent tattoo on the torso/arms were tested during cycling, running, or fitness sessions (26 ± 4 °C and 54 ± 13% relative humidity). Forty-eight tattoos with a range of ink colors, ages (3 weeks to 20 years), and densities (10-100%) were included. Before exercise, the skin was cleaned with alcohol and patches (3 M Tegaderm + Pad) were placed on the tattooed and contralateral non-tattooed skin. LSR was calculated from sweat mass (0.80 ± 0.31 g), patch surface area (11.9 cm2), and duration (62 ± 14 min). Sweat [Na+], [Cl-], and [K+] were measured via ion chromatography. RESULTS: Based on the analysis of variance results, there were no differences between tattooed and non-tattooed skin for LSR (1.16 ± 0.52 vs. 1.12 ± 0.53 mg/cm2/min; p = 0.51), sweat [Na+] (60.2 ± 23.5 vs. 58.5 ± 22.7 mmol/L; p = 0.27), sweat [Cl-] (52.1 ± 22.4 vs. 50.6 ± 22.0 mmol/L; p = 0.31), or sweat [K+] (5.8 ± 1.6 vs. 5.9 ± 1.4 mmol/L; p = 0.31). Multiple regression analyses suggested that younger tattoos were associated with higher sweat [Na+] (p = 0.045) and colorful tattoos were associated with higher sweat [Cl-] (p = 0.04) compared with contralateral non-tattooed skin. Otherwise, there were no effects of LSR or tattoo characteristics on regression models for LSR or sweat electrolyte concentrations. CONCLUSION: There were no effects of tattoos on LSR and sweat [K+] during exercise-induced sweating, but tattoo age and color had small effects on sweat [Na+] and sweat [Cl-], respectively. CLINICAL TRIAL IDENTIFIERS: NCT04240951 was registered on January 27, 2020 and NCT04920266 was registered on June 9, 2021.

Sweating Rate and Sweat Chloride Concentration of Elite Male Basketball Players Measured With a Wearable Microfluidic Device Versus the Standard Absorbent Patch Method.

The purpose of this study was to compare a wearable microfluidic device and standard absorbent patch in measuring local sweating rate (LSR) and sweat chloride concentration ([Cl-]) in elite basketball players. Participants were 53 male basketball players (25 ± 3 years, 92.2 ± 10.4 kg) in the National Basketball Association's development league. Players were tested during a moderate-intensity, coach-led practice (98 ± 30 min, 21.0 ± 1.2 °C). From the right ventral forearm, sweat was collected using an absorbent patch (3M Tegaderm™ + Pad). Subsequently, LSR and local sweat [Cl-] were determined via gravimetry and ion chromatography. From the left ventral forearm, LSR and local sweat [Cl-] were measured using a wearable microfluidic device and associated smartphone application-based algorithms. Whole-body sweating rate (WBSR) was determined from pre- to postexercise change in body mass corrected for fluid/food intake (ad libitum), urine loss, and estimated respiratory water and metabolic mass loss. The WBSR values predicted by the algorithms in the smartphone application were also recorded. There were no differences between the absorbent patch and microfluidic patch for LSR (1.25 ± 0.91 mg·cm-2·min-1 vs. 1.14 ±0.78 mg·cm-2·min-1, p = .34) or local sweat [Cl-] (30.6 ± 17.3 mmol/L vs. 29.6 ± 19.4 mmol/L, p = .55). There was no difference between measured and predicted WBSR (0.97 ± 0.41 L/hr vs. 0.89 ± 0.35 L/hr, p = .22; 95% limits of agreement = 0.61 L/hr). The wearable microfluidic device provides similar LSR, local sweat [Cl-], and WBSR results compared with standard field-based methods in elite male basketball players during moderate-intensity practices.

Predicted sweat rates for group water planning in sport: accuracy and application.

This study tested the accuracy of a novel, limited-availability web application (H2Q™) for predicting sweat rates in a variety of sports using estimates of energy expenditure and air temperature only. The application of predictions for group water planning was investigated for soccer match play. Fourteen open literature studies were identified where group sweat rates were reported (n = 20 group means comprising 230 individual observations from 179 athletes) with fidelity. Sports represented included: walking, cycling, swimming, and soccer match play. The accuracy of H2Q™ sweat rates was tested by comparing to measured group sweat rates using the concordance correlation coefficient (CCC) with 95% confidence interval [CI]. The relative absolute error (RAE) with 95% [CI] was also assessed, whereby the mean absolute error was expressed relative to an acceptance limit of 0.250 L/h. The CCC was 0.98 [0.95, 0.99] and the RAE was 0.449 [0.279, 0.620], indicating that the prediction error was on average 0.112 L/h. The RAE was < 1.0 for 19/20 observations (95%). Drink volumes modeled as a proxy for sweat losses during soccer match play prevented dehydration (< 1% loss of body mass). The H2Q™ web application demonstrated high group sweat prediction accuracy for the variety of sports activities tested. Water planning for soccer match play suggests the feasibility of easily and accurately predicting sweat rates to plan group water needs and promote optimal hydration in training and/or competition.

Physiological mechanisms determining eccrine sweat composition.

PURPOSE: The purpose of this paper is to review the physiological mechanisms determining eccrine sweat composition to assess the utility of sweat as a proxy for blood or as a potential biomarker of human health or nutritional/physiological status. METHODS: This narrative review includes the major sweat electrolytes (sodium, chloride, and potassium), other micronutrients (e.g., calcium, magnesium, iron, copper, zinc, vitamins), metabolites (e.g., glucose, lactate, ammonia, urea, bicarbonate, amino acids, ethanol), and other compounds (e.g., cytokines and cortisol). RESULTS: Ion membrane transport mechanisms for sodium and chloride are well established, but the mechanisms of secretion and/or reabsorption for most other sweat solutes are still equivocal. Correlations between sweat and blood have not been established for most constituents, with perhaps the exception of ethanol. With respect to sweat diagnostics, it is well accepted that elevated sweat sodium and chloride is a useful screening tool for cystic fibrosis. However, sweat electrolyte concentrations are not predictive of hydration status or sweating rate. Sweat metabolite concentrations are not a reliable biomarker for exercise intensity or other physiological stressors. To date, glucose, cytokine, and cortisol research is too limited to suggest that sweat is a useful surrogate for blood. CONCLUSION: Final sweat composition is not only influenced by extracellular solute concentrations, but also mechanisms of secretion and/or reabsorption, sweat flow rate, byproducts of sweat gland metabolism, skin surface contamination, and sebum secretions, among other factors related to methodology. Future research that accounts for these confounding factors is needed to address the existing gaps in the literature.

Permanent tattooing has no impact on local sweat rate, sweat sodium concentration and skin temperature or prediction of whole-body sweat sodium concentration during moderate-intensity cycling in a warm environment.

PURPOSES: This study investigated the impact of permanently tattooed skin on local sweat rate, sweat sodium concentration and skin temperature and determined whether tattoos alter the relationship between local and whole-body sweat sodium concentration. METHODS: Thirteen tattooed men (27 ± 6 years) completed a 1 h (66 ± 4% of [Formula: see text]) cycling trial at 32 °C, 35% relative humidity. Sweat rate and sweat sodium concentration were measured using the whole-body washdown and local absorbent patch techniques. Patches and skin-temperature probes were applied over the right/left thighs and tattooed/non-tattooed (contralateral) regions. RESULTS: Local sweat rates did not differ (p > 0.05) between the right (1.11 ± 0.38) and left (1.21 ± 0.37) thighs and the permanently tattooed (1.93 ± 0.82) and non-tattooed (1.72 ± 0.81 mg cm-2 min-1) regions. There were no differences in local sweat sodium concentration between the right (58.2 ± 19.4) and left (55.4 ± 20.3) thighs and the permanently tattooed (73.0 ± 22.9) and non-tattooed (70.2 ± 18.9 mmol L-1) regions. Difference in local skin temperature between the right and left thighs (- 0.043) was similar to that between the permanently tattooed and non-tattooed (- 0.023 °C) regions. Prediction of whole-body sweat sodium concentration for the permanently tattooed (41.0 ± 6.7) and the non-tattooed (40.2 ± 5.3 mmol L-1) regions did not differ. CONCLUSION: Permanent tattoos do not alter local sweat rate, sweat sodium concentration or local skin temperature during moderate-intensity cycling exercise in a warm environment. Results from a patch placed over a tattooed surface correctly predicts whole-body sweat sodium concentration from an equation developed from a non-tattooed region.

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.

Normative data for sweating rate, sweat sodium concentration, and sweat sodium loss in athletes: An update and analysis by sport.

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.

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.

Sweat Sodium Concentration: Inter-Unit Variability of a Low Cost, Portable, and Battery Operated Sodium Analyzer.

The B-722 Laqua Twin is a low cost, portable, and battery operated sodium analyzer, which can be used for the assessment of sweat sodium concentration. The Laqua Twin is reliable and provides a degree of accuracy similar to more expensive analyzers; however, its interunit measurement error remains unknown. The purpose of this study was to compare the sodium concentration values of 70 sweat samples measured using three different Laqua Twin units. Mean absolute errors, random errors and constant errors among the different Laqua Twins ranged respectively between 1.7 mmol/L to 3.5 mmol/L, 2.5 mmol/L to 3.7 mmol/L and -0.6 mmol/L to 3.9 mmol/L. Proportional errors among Laqua Twins were all < 2%. Based on a within-subject biological variability in sweat sodium concentration of ± 12%, the maximal allowable imprecision among instruments was considered to be £ 6%. In that respect, the within (2.9%), between (4.5%), and total (5.4%) measurement error coefficient of variations were all < 6%. For a given sweat sodium concentration value, the largest observed difference in mean and lower and upper bound error of measurements among instruments were, respectively, 4.7 mmol/L, 2.3 mmol/L, and 7.0 mmol/L. In conclusion, our findings show that the interunit measurement error of the B-722 Laqua Twin is low and methodologically acceptable.

Normative data for regional sweat sodium concentration and whole-body sweating rate in athletes.

The purpose of this study was to establish normative data for regional sweat sodium concentration ([Na+]) and whole-body sweating rate in athletes. Data from 506 athletes (367 adults, 139 youth; 404 male, 102 female) were compiled from observational athlete testing for a retrospective analysis. The participants were skill/team-sport (including American football, baseball, basketball, soccer and tennis) and endurance (including cycling, running and triathlon) athletes exercising in cool to hot environmental conditions (15-50 °C) during training or competition in the laboratory or field. A standardised regional absorbent patch technique was used to determine sweat [Na+] on the dorsal mid-forearm. Whole-body sweat [Na+] was predicted using a published regression equation (y = 0.57x+11.05). Whole-body sweating rate was calculated from pre- to post-exercise change in body mass, corrected for fluid/food intake (ad libitum) and urine output. Data are expressed as mean ± SD (range). Forearm sweat [Na+] and predicted whole-body sweat [Na+] were 43.6 ± 18.2 (12.6-104.8) mmol · L(-1) and 35.9 ± 10.4 (18.2-70.8) mmol · L(-1), respectively. Absolute and relative whole-body sweating rates were 1.21 ± 0.68 (0.26-5.73) L · h(-1) and 15.3 ± 6.8 (3.3-69.7) ml · kg(-1) · h(-1), respectively. This retrospective analysis provides normative data for athletes' forearm and predicted whole-body sweat [Na+] as well as absolute and relative whole-body sweating rate across a range of sports and environmental conditions.

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.

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.

Recovery interventions and strategies for improved tennis performance.

Improving the recovery capabilities of the tennis athlete is receiving more emphasis in the research communities, and also by practitioners (coaches, physical trainers, tennis performance specialists, physical therapists, etc). The purpose of this article was to review areas of recovery to limit the severity of fatigue and/or speed recovery from fatigue. This review will cover four broad recovery techniques commonly used in tennis with the belief that the interventions may improve athlete recovery and therefore improve adaptation and future performance. The four areas covered are: (1) temperature-based interventions, (2) compressive clothing, (3) electronic interventions and (4) nutritional interventions.

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.

No effect of tattoos on local sweat concentrations of select cytokines, cortisol, glucose, blood urea nitrogen, or lactate during exercise.

Due to growing interest in the investigation of exercise induced sweat biomarkers to assess an individual's health and the increasing prevalence of tattoos in the world's population, investigators sought to determine whether local sweat concentrations and excretion rates of epidermal growth factor (EGF), interleukin (IL) -1α, IL-6, IL-8, cortisol, glucose, blood urea nitrogen (BUN), and lactate differ between tattooed and contralateral non-tattooed skin during exercise. Sixteen recreational exercisers [female (50%)] (age = 25-48 years) with ≥ 1 unilateral permanent tattoo [median tattoo age = 6 years, IQR = 5] on the arm/torso completed an outdoor group fitness session. There were no significant differences between tattooed and non-tattooed skin for sweat EGF, IL-1α, IL-8, cortisol, glucose, BUN, or lactate concentrations. There were no significant differences between tattooed and non-tattooed skin for sweat EGF, IL-1α, IL-8, cortisol, glucose, BUN, or lactate excretion rate. Findings suggest that permanent tattoos older than 1 year may not impact local sweat EGF, IL-1α, IL-8, cortisol, glucose, BUN, and lactate concentrations or excretion rates during exercise.Clinical trial identifier NCT04920266 was registered on June 9, 2021.

Mild dehydration does not alter acute changes in sweat electrolyte concentrations during exercise.

The purpose of this study was to determine the effect of hydration status on the change in sweat sodium (Na+), chloride (Cl-), and potassium (K+) concentrations during exercise-heat stress. Fifteen subjects (Six female, nine male; 29 ± 9 y; 71 ± 14 kg) completed 90 min of cycling (81% HRmax) in the heat (~33°C, 42% rh) with fluid replacement to maintain euhydration (EUH) or without fluid to dehydrate to 2.4 ± 0.4% body mass loss (DEH). Sweat was collected from the forehead (FH), right scapula (SCAP), and left (LVFA) and right (RVFA) ventral forearms using the absorbent pad technique at the beginning (0-30 min) and end of exercise (60-90 min). Sweat was analyzed for Na+, Cl-, and K+ concentrations using ion chromatography. Data are reported as mean ± SD or median ± IQR. There were no differences (Paired t-tests or Wilcoxon signed-rank tests) between EUH and DEH in the change in sweat Na+ (FH: 24.3 ± 21.5 vs. 30.8 ± 22.4 mmol/L; SCAP: 9.7 ± 6.2 vs. 9.6 ± 8.2 mmol/L; LVFA: 7.5 ± 6.0 vs. 5.6 ± 5.9 mmol/L; RVFA: 8.2 ± 8.6 vs. 7.8 ± 5.2 mmol/L), sweat Cl-, or sweat K+ at any site (p = 0.07-0.99). The change in sweat electrolyte concentrations during 90 min of exercise in the heat was not significantly influenced by mild dehydration in recreational to moderately-trained male and female athletes.

Accuracy of a smart bottle in measuring fluid intake by American football players during pre-season training.

Rehydration is important for athlete performance and recovery. However, it can be challenging to follow appropriate fluid replacement practices due in part to difficulties in tracking fluid intake in real time. The purpose of this study was to determine the accuracy of a smart bottle in measuring fluid intake during exercise. Thirty male American football athletes drank from bottles equipped with a smart cap during outdoor pre-season practices (110 ± 30 min; 29.3 ± 3.0 °C; 75 ± 11% rh). The cap technology included optical sensors, microprocessors, batteries, and wireless connectivity that transmitted fluid volume data to a smartphone application in real-time. Reference measurements of fluid intake from the smart bottle were determined by gravimetry followed by conversion to volume using the density of the fluid consumed. There was no significant mean difference in fluid intake between the smart bottle and reference method (1220 ± 371 ml vs. 1236 ± 389 ml, p = 0.39 paired t test). Bland-Altman 95% limits of agreement between methods was - 212 to 180 ml. The smart bottle provided accurate measurements of fluid intake during exercise in real-world field conditions on a group level and within limits of agreement of - 212 to 180 ml (or approximately ± 15% of overall fluid intake) on an individual level.

In-Season Nutrition Strategies and Recovery Modalities to Enhance Recovery for Basketball Players: A Narrative Review.

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.

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+].