Iterative assessment of a sports rehydration beverage containing a novel amino acid formula on water uptake kinetics.

PURPOSE: Rapid gastric emptying and intestinal absorption of beverages is essential for rapid rehydration, and certain amino acids (AA) may augment fluid delivery. Three sugar-free beverages, containing differing AA concentrations (AA + PZ), were assessed for fluid absorption kinetics against commercial sugar-free (PZ, GZ) and carbohydrate-containing (GTQ) beverages. METHODS: Healthy individuals (n = 15-17 per study) completed three randomised trials. Three beverages (550-600 mL) were ingested in each study (Study 1: AA + PZ [17.51 g/L AA], PZ, GZ; Study 2: AA + PZ [6.96 g/L AA], PZ, GZ; Study 3: AA + PZ [3.48 g/L AA], PZ, GTQ), containing 3.000 g deuterium oxide (D2O). Blood samples were collected pre-, 2-min, 5-min, and every 5-min until 60-min post-ingestion to quantify maximal D2O enrichment (Cmax), time Cmax occurred (Tmax) and area under the curve (AUC). RESULTS: Study 1: AUC (AA + PZ: 15,184 ± 3532 δ‰ vs. VSMOW; PZ: 17,328 ± 3153 δ‰ vs. VSMOW; GZ: 17,749 ± 4204 δ‰ vs. VSMOW; P ≤ 0.006) and Tmax (P ≤ 0.005) were lower for AA + PZ vs. PZ/GZ. Study 2: D2O enrichment characteristics were not different amongst beverages (P ≥ 0.338). Study 3: Cmax (AA + PZ: 440 ± 94 δ‰ vs. VSMOW; PZ: 429 ± 83 δ‰ vs. VSMOW; GTQ: 398 ± 81 δ‰ vs. VSMOW) was greater (P = 0.046) for AA + PZ than GTQ, with no other differences (P ≥ 0.106). CONCLUSION: The addition of small amounts of AA (3.48 g/L) to a sugar-free beverage increased fluid delivery to the circulation compared to a carbohydrate-based beverage, but greater amounts (17.51 g/L) delayed delivery.

Forecasting individual exercise sweat losses from forecast air temperature and energy expenditure.

Introduction: Recent success in predicting individual sweat losses from air temperature and energy expenditure measurements suggests a potential for forecasting individual sweat losses for future combinations of environment and exercise. The purpose of this study is to determine the plausibility of accurately forecasting exercise sweat losses from meteorological air temperature forecasts and individual running energy expenditure forecasts. The potential impact on plasma sodium is also estimated when setting drinking rates equal to forecast sweat losses. Materials and methods: Individual exercise sweat losses (equated to water needs) and energy expended while running were measured in 33 participants along with air temperature and compared with forecasts of the same. Forecast inputs were used in a web app to forecast exercise sweat losses for comparison with observed values. The bias between forecast and observed exercise sweat losses was used to calculate the potential drinking impact on plasma sodium. Results: The concordance correlation coefficient between forecast and observed values was 0.95, 0.96, and 0.91 for air temperature, energy expenditure, and exercise sweat losses, respectively, indicating excellent agreement and no significant differences observed via t-test. Perfect matching of water intake to sweat losses would lower plasma sodium concentrations from 140 to 138 mmol/L; calculations using the 95% limits of agreement for bias showed that drinking according to forecast exercise sweat losses would alter plasma sodium concentrations from 140 to between 136 and 141 mmol/L. Conclusions: The outcomes support the strong potential for accurately forecasting exercise sweat losses from commonly available meteorological air temperature forecasts and energy expenditure from forecast running distance.

The accurate prediction of sweat rate from energy expenditure and air temperature: a proof-of-concept study.

This proof-of-concept study used a web application to predict runner sweat losses using only energy expenditure and air temperature. A field study (FS) of n = 37 runners was completed with n = 40 sweat loss observations measured over 1 h (sweat rate, SR). Predictions were also compared with 10 open literature (OL) studies in which individual runner SR was reported (n = 82; 109 observations). Three prediction accuracy metrics were used: for FS, the mean absolute error (MAE) and concordance correlation coefficient (CCC) were calculated to include a 95% confidence interval [CI]; for OL, the percentage concordance (PC) was examined against calculation of accumulated under- and over-drinking potential. The MAE for FS runners was 0.141 kg [0.105, 0.177], which was less than estimated scale weighing error on 85% of occasions. The CCC was 0.88 [0.82, 0.93]. The PC for OL was 96% for avoidance of both under- and over-drinking and 93% overall. All accuracy metrics and their CIs were below acceptable error tolerance. Input errors of ±10% and ±1 °C for energy expenditure and air temperature dropped the PC to between 84% and 90%. This study demonstrates the feasibility of accurately predicting SR from energy expenditure and air temperature alone. Novelty Results demonstrate that accurate runner SR prediction is possible with knowledge of only energy expenditure and air temperature. SR prediction error was smaller than scale weighing error in 85% of observations. Accurate runner SR prediction could help mitigate the common risks of over- and under-drinking.

Noninvasive assessment of extracellular and intracellular dehydration in healthy humans using the resistance-reactance-score graph method.

BACKGROUND: Few dehydration assessment measures provide accurate information; most are based on reference change values and very few are diagnostically accurate from a single observation or measure. Bioelectrical impedance may lack the precision to detect common forms of dehydration in healthy individuals. Limitations in bioimpedance may be addressed by a unique resistance-reactance (RXc)-score graph method, which transforms vector components into z scores for use with any impedance analyzer in any population. OBJECTIVE: We tested whether the RXc-score graph method provides accurate single or serial assessments of dehydration when compared with gold-standard measures of total body water by using stable isotope dilution (deuterium oxide) combined with body-weight changes. DESIGN: We retrospectively analyzed data from a previous study in which 9 healthy young men participated in 3 trials: euhydration (EUH), extracellular dehydration (ED; via a diuretic), and intracellular dehydration (ID; via exercise in the heat). RESULTS: Participants lost 4-5% of their body weight during the dehydration trials; volume loss was similar between trials (ID compared with ED group: 3.5 ± 0.8 compared with 3.0 ± 0.6 L; P > 0.05). Despite significant losses of body water, most RXc vector scores for ED and ID groups were classified as "normal" (within the 75% population tolerance ellipse). However, directional displacement of vectors was consistent with loss of volume in both ED and ID conditions compared with the EUH condition and tended to be longer in ED than in ID conditions (P = 0.054). CONCLUSIONS: We conclude that, whereas individual RXc-score graph values do not provide accurate detection of dehydration from single measurements, directional changes in vector values from serial measurements are consistent with fluid loss for both ED and ID conditions. The RXc-score graph method may therefore alert clinicians to changes in hydration state, which may bolster the interpretation of other recognized change measures of hydration.

Human Performance Optimization Metrics: Consensus Findings, Gaps, and Recommendations for Future Research.

Human performance optimization (HPO) is defined as "the process of applying knowledge, skills and emerging technologies to improve and preserve the capabilities of military members, and organizations to execute essential tasks." The lack of consensus for operationally relevant and standardized metrics that meet joint military requirements has been identified as the single most important gap for research and application of HPO. In 2013, the Consortium for Health and Military Performance hosted a meeting to develop a toolkit of standardized HPO metrics for use in military and civilian research, and potentially for field applications by commanders, units, and organizations. Performance was considered from a holistic perspective as being influenced by various behaviors and barriers. To accomplish the goal of developing a standardized toolkit, key metrics were identified and evaluated across a spectrum of domains that contribute to HPO: physical performance, nutritional status, psychological status, cognitive performance, environmental challenges, sleep, and pain. These domains were chosen based on relevant data with regard to performance enhancers and degraders. The specific objectives at this meeting were to (a) identify and evaluate current metrics for assessing human performance within selected domains; (b) prioritize metrics within each domain to establish a human performance assessment toolkit; and (c) identify scientific gaps and the needed research to more effectively assess human performance across domains. This article provides of a summary of 150 total HPO metrics across multiple domains that can be used as a starting point-the beginning of an HPO toolkit: physical fitness (29 metrics), nutrition (24 metrics), psychological status (36 metrics), cognitive performance (35 metrics), environment (12 metrics), sleep (9 metrics), and pain (5 metrics). These metrics can be particularly valuable as the military emphasizes a renewed interest in Human Dimension efforts, and leverages science, resources, programs, and policies to optimize the performance capacities of all Service members.

Spot Urine Concentrations Should Not be Used for Hydration Assessment: A Methodology Review.

A common practice in sports science is to assess hydration status using the concentration of a single spot urine collection taken at any time of day for comparison against concentration (specific gravity, osmolality, color) thresholds established from first morning voids. There is strong evidence that this practice can be confounded by fluid intake, diet, and exercise, among other factors, leading to false positive/negative assessments. Thus, the purpose of this paper is to provide a simple explanation as to why this practice leads to erroneous conclusions and should be curtailed in favor of consensus hydration assessment recommendations.

Dehydration: physiology, assessment, and performance effects.

This article provides a comprehensive review of dehydration assessment and presents a unique evaluation of the dehydration and performance literature. The importance of osmolality and volume are emphasized when discussing the physiology, assessment, and performance effects of dehydration. The underappreciated physiologic distinction between a loss of hypo-osmotic body water (intracellular dehydration) and an iso-osmotic loss of body water (extracellular dehydration) is presented and argued as the single most essential aspect of dehydration assessment. The importance of diagnostic and biological variation analyses to dehydration assessment methods is reviewed and their use in gauging the true potential of any dehydration assessment method highlighted. The necessity for establishing proper baselines is discussed, as is the magnitude of dehydration required to elicit reliable and detectable osmotic or volume-mediated compensatory physiologic responses. The discussion of physiologic responses further helps inform and explain our analysis of the literature suggesting a ≥ 2% dehydration threshold for impaired endurance exercise performance mediated by volume loss. In contrast, no clear threshold or plausible mechanism(s) support the marginal, but potentially important, impairment in strength, and power observed with dehydration. Similarly, the potential for dehydration to impair cognition appears small and related primarily to distraction or discomfort. The impact of dehydration on any particular sport skill or task is therefore likely dependent upon the makeup of the task itself (e.g., endurance, strength, cognitive, and motor skill).

Assessment of extracellular dehydration using saliva osmolality.

INTRODUCTION: When substantial solute losses accompany body water an isotonic hypovolemia (extracellular dehydration) results. The potential for using blood or urine to assess extracellular dehydration is generally poor, but saliva is not a simple ultra-filtrate of plasma and the autonomic regulation of salivary gland function suggests the possibility that saliva osmolality (Sosm) may afford detection of extracellular dehydration via the influence of volume-mediated factors. PURPOSE: This study aimed to evaluate the assessment of extracellular dehydration using Sosm. In addition, two common saliva collection methods and their effects on Sosm were compared. METHODS: Blood, urine, and saliva samples were collected in 24 healthy volunteers during paired euhydration and dehydration trials. Furosemide administration and 12 h fluid restriction were used to produce extracellular dehydration. Expectoration and salivette collection methods were compared in a separate group of eight euhydrated volunteers. All comparisons were made using paired t-tests. The diagnostic potential of body fluids was additionally evaluated. RESULTS: Dehydration (3.1 ± 0.5% loss of body mass) decreased PV (-0.49 ± 0.12 L; -15.12 ± 3.94% change), but Sosm changes were marginal (<10 mmol/kg) and weakly correlated with changes in absolute or relative PV losses. Overall diagnostic accuracy was poor (AUC = 0.77-0.78) for all body fluids evaluated. Strong agreement was observed between Sosm methods (Expectoration: 61 ± 10 mmol/kg, Salivette: 61 ± 8 mmol/kg, p > 0.05). CONCLUSIONS: Extracelluar dehydration was not detectable using plasma, urine, or saliva measures. Salivette and expectoration sampling methods produced similar, consistent results for Sosm, suggesting no methodological influence on Sosm.

Physiologic basis for understanding quantitative dehydration assessment.

Dehydration (body water deficit) is a physiologic state that can have profound implications for human health and performance. Unfortunately, dehydration can be difficult to assess, and there is no single, universal gold standard for decision making. In this article, we review the physiologic basis for understanding quantitative dehydration assessment. We highlight how phenomenologic interpretations of dehydration depend critically on the type (dehydration compared with volume depletion) and magnitude (moderate compared with severe) of dehydration, which in turn influence the osmotic (plasma osmolality) and blood volume-dependent compensatory thresholds for antidiuretic and thirst responses. In particular, we review new findings regarding the biological variation in osmotic responses to dehydration and discuss how this variation can help provide a quantitative and clinically relevant link between the physiology and phenomenology of dehydration. Practical measures with empirical thresholds are provided as a starting point for improving the practice of dehydration assessment.

Hydration assessment using the cardiovascular response to standing.

The cardiovascular response to standing (sit-to-stand change in heart rate; SSΔHR) is commonly employed as a screening tool to detect hypohydration (body water deficit). No study has systematically evaluated SSΔHR cut points using different magnitudes or different types of controlled hypohydration. The objective of this study was to determine the diagnostic accuracy of the often proposed 20 b/min SSΔHR cut point using both hypertonic and isotonic models of hypohydration. Thirteen healthy young adults (8M, 5F) underwent three bouts of controlled hypohydration. The first bout used sweating to elicit large losses of body water (mass) (>3 % sweat). The second two bouts were matched to elicit 3 % body mass losses (3 % diuretic; 3 % sweat). A euhydration control trial (EUH) was paired with each hypohydration trial for a total of six trials. Heart rate was assessed after 3-min sitting and after 1-min standing during all trials. SSΔHR was compared among trials, and receiver operator characteristic curve analysis was used to determine diagnostic accuracy of the 20 b/min SSΔHR cut point. Volunteers lost 4.5 ± 1.1, 3.0 ± 0.6, and 3.2 ± 0.6 % body mass during >3 % sweat, 3 % diuretic, and 3 % sweat trials, respectively. SSΔHR (b/min) was 9 ± 8 (EUH), 20 ± 12 (>3 % sweat; P < 0.05 vs. EUH), 17 ± 7 (3 % diuretic; P < 0.05 vs. EUH), and 13 ± 11 (3 % sweat). The 20 beats/min cut point had high specificity (90 %) but low sensitivity (44 %) and overall diagnostic accuracy of 67 %. SSΔHR increased significantly in response to severe hypertonic hypohydration and moderate isotonic hypohydration, but not moderate hypertonic hypohydration. However, the 20 beats/min cut point afforded only marginal diagnostic accuracy.

Biological variation and diagnostic accuracy of dehydration assessment markers.

BACKGROUND: Well-recognized markers for static (one time) or dynamic (monitoring over time) dehydration assessment have not been rigorously tested for their usefulness in clinical, military, and sports medicine communities. OBJECTIVE: This study evaluated the components of biological variation and the accuracy of potential markers in plasma, urine, saliva, and body mass (B(m)) for static and dynamic dehydration assessment. DESIGN: We studied 18 healthy volunteers (13 men and 5 women) while carefully controlling hydration and numerous preanalytic factors. Biological variation was determined over 3 consecutive days by using published methods. Atypical values based on statistical deviations from a homeostatic set point were examined. Measured deviations in body fluid were produced by using a separate, prospective dehydration experiment and evaluated by receiver operating characteristic (ROC) analysis to quantify diagnostic accuracy. RESULTS: All dehydration markers displayed substantial individuality and one-half of the dehydration markers displayed marked heterogeneity of intraindividual variation. Decision levels for all dehydration markers were within one SD of the ROC criterion values, and most levels were nearly identical to the prospective group means after volunteers were dehydrated by 1.8-7.0% of B(m). However, only plasma osmolality (P(osm)) showed statistical promise for use in the static dehydration assessment. A diagnostic decision level of 301 plusmn 5 mmol/kg was proposed. Reference change values of 9 mmol/kg (P(osm)), 0.010 [urine specific gravity (U(sg))], and 2.5% change in B(m) were also statistically valid for dynamic dehydration assessment at the 95% probability level. CONCLUSIONS: P(osm) is the only useful marker for static dehydration assessment. P(osm), U(sg), and B(m) are valid markers in the setting of dynamic dehydration assessment.