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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 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 FROM AUTHOR]

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 30-60 g carbohydrate/hr during practice/competition. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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

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. [ABSTRACT FROM AUTHOR]

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%, -1.2-2.7%) and protein (14.1%, -3.2-4.5%) but was significant for energy (14.4%, 1.2-9.3%). 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. [ABSTRACT FROM AUTHOR]

The article presents a study which examined the use of skin-interfaced wearable microfluidic device and smartphone image processing platform to analyze regional sweating rate and sweat chloride concentration (CI-) for sports science applications. The aim is to determine the correct time and amount of personalized fluid-electrolyte intake of athletes participating in competitive sports to maintain their proper hydration and electrolyte balance.

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. [ABSTRACT FROM AUTHOR]

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 V̇o 2max : 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 r 2 = 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 r 2 = 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 ( r 2 = 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 V̇o 2max ) had no association with whole body sweat [Na + ]. Taken together, the models explained 17%-23% of the variation in whole body sweat [Na + ].

Abstract 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.