Sleep habits of high school student-athletes and nonathletes during a semester.

STUDY OBJECTIVES: Lack of sleep has been shown to be harmful to athletic and academic performance as well as health and well-being. The primary purpose of this study was to analyze the sleep and physical activity differences between US high school student-athletes and nonathletes during a semester of school and competition. METHODS: Participants included 34 student-athletes (18 males and 16 females), age 15.8 ± 0.8 years, and 38 nonathletes (10 males and 28 females), age 16.3 ± 0.7 years. Objective sleep and physical activity outcomes were collected using Fitbit wrist-worn activity trackers for 8-14 consecutive days and nights, measuring total sleep time, sleep efficiency, bedtimes, wake times, and steps counted. RESULTS: Student-athletes and nonathletes did not differ in total sleep time (440.4 ± 46.4 vs 438.1 ± 41.7 min, P = .82) and sleep efficiency (93.6 ± 2.3 vs 92.9 ± 2.3%, P = .20). Fitbit data revealed that 79% of student-athletes and 87% of nonathletes failed to get greater than the minimally recommended 8 hours of total sleep time per night. Student-athletes had significantly more steps per day (10,163 ± 2,035 vs 8,418 ± 2,489, P < .01). Student-athletes had earlier bedtimes and wake times. Earlier bedtimes were significantly correlated with increased total sleep time (P < .01). Earlier wake times were significantly correlated to increased steps per day (P < .01). CONCLUSIONS: Participation in high school sports may not have a detrimental effect on a student's sleep habits. High school students are not meeting the recommended 8-10 hours of sleep per night. Going to bed and waking up early were linked to healthier outcomes. Consistent and earlier sleep/wake schedules may optimize students sleep and health. CITATION: Ungaro CT, De Chavez PJD. Sleep habits of high school student-athletes and nonathletes during a semester. J Clin Sleep Med. 2022;18(9):2189-2196.

Skin-interfaced microfluidic system with personalized sweating rate and sweat chloride analytics for sports science applications.

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.

Cross-validation of equations to predict whole-body sweat sodium concentration from regional measures during exercise.

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.

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.

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.

Body map of regional vs. whole body sweating rate and sweat electrolyte concentrations in men and women during moderate exercise-heat stress.

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.

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.

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 reliability of a field technique for sweat Na+ and K+ analysis during exercise in a hot-humid environment.

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.