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Background: The ability to monitor changes in daily hydration status is critical for human health and performance. Monitoring changes in body weight (W), urine color (U), and thirst (T) was proposed as a simple, low-cost method for daily hydration monitoring [1]. However, the ability of these metrics to accurately predict 24-h hydration status is yet to be fully tested. Objective: The purpose of this study was to assess the degree to which daily monitoring of W, U, and T (i.e., the WUT model) could accurately predict 24-h hydration status. Methods: Thirty-five male and female adults (age: 23.4 ± 4.1 years, height: 173.0 ± 10.3 cm, mass: 77.2 ± 18.2 kg, body fat: 18.4 ± 8.4%) were monitored for 8 consecutive days. Assessments on each morning included a 24-h urine sample for urine osmolality (UOSM24), a first void spot urine for urine color (U), nude body weight (W), and perceived thirst (T) using a 1–9 Likert scale. On days 7 and 8, a blood sample was taken for copeptin assessment [2]. If UOSM24 was >800 mOsm•kg−1 on any day, the participants were classified as hypohydrated. Multiple research questions were explored. First, models tested the degree to which W, U, and T could predict UOSM24 (RQ1). Classification models assessed the ability to predict whether an individual was hypohydrated (UOSM24 >800 mOsm•kg−1; RQ2). Last, models tested the degree to which W, U, and T could predict concentrations of copeptin (RQ3). For each question, 4 separate modeling approaches were used: linear regression (LR), elastic net regression (EN), extreme gradient boosted random forests (XB), and a single hidden layer neural network (NN). Eighty percent of the data (RQ1/RQ2 n = 207, RQ3 n = 49) were used to train the models, while 20% of the data were held out (RQ1/RQ2 n = 51, RQ3 n = 10) for model validation. Within the training data, bootstrap samples (XB) and cross-fold validation (EN and NN) samples were used to optimize model hyperparameters. Prediction accuracy for regression analyses were assessed via R2 and root mean square error (RMSE), and for classification analyses via area under the curve of the receiver operating characteristic. Results: The results demonstrated that the NN model performed best when predicting UOSM24 (R2 = 0.532, RMSE = 228 mOsm•kg−1), but all models had relatively poor fit and large errors during validation. All classification models were moderately effective at discriminating between the binary hydration status of individuals (area under the curve of the receiver operating characteristic range: 0.661–0.696). Linear regression (Fig. 1; R2 = 0.929, RMSE = 1.49 pmol•L−1), XB (R2 = 0.834, RMSE = 3.11 pmol•L−1), EN (R2 = 0.890, RMSE = 2.13 pmol•L−1), and NN (R2 = 0.930, RMSE = 1.84 pmol•L−1) were able to predict copeptin concentrations with relatively low error. Conclusions: The WUT model accurately predicts copeptin concentrations on out of training data observations, suggesting that first morning measures of W, U, and T are effective for tracking hydration status by indirectly monitoring arginine vasopressin. More research is needed to determine potential cut-scores of predicted copeptin levels to aid practitioner decisions. [ABSTRACT FROM AUTHOR]

PURPOSE: Animal research suggests that variations in daily dietary macronutrient distribution may impact fluid consumption. However, this has not been fully investigated in humans. Thus, the purpose of this study was to determine the impact of dietary intake on fluid intake, thirst, and urinary hydration biomarkers. METHODS: Thirty-four participants (44% female; age: 23±4 years; height: 172.9±10.3cm; body mass: 77.2±18.1kg; body fat: 18.4± 8.4%) recorded their food and fluid intake, and provided a 24h urine sample over seven consecutive days. Urinary hydration biomarkers included urine volume (UVOL), urine osmolality (UOSM), urine specific gravity (USG), and urine color (UCOL). Each morning, participants completed two subjective ratings of perceived thirst; a 9-point Likert scale, and 100mm visual analog scales comprised of six indices (thirstiness, pleasantness, dryness, taste, fullness, sickness). RESULTS: Mean weekly macronutrient percentages and total calorie intake were used in multiple regression models as predictors of average total fluid intake, thirst, and urinary hydration markers. Throughout the week, participants reported consuming, on average, 1945±429 calories (36.5±4.4% fat, 44.3±6.3% carbohydrate, 18.5±4.5% protein) and 2508±1122mL fluid per day. Mean 7-day 24h urinary hydration markers were UVOL:1724±850mL, USG :1.017±0.005, UOSM: 565±212, UCOL: 3.89±0.99. Dietary intake that was higher in percentage of fat consumed was associated with higher ratings of "fullness" on morning thirst scale sub ratings (adj R² = 0.1181, p =0.0167). Consuming a greater proportion of calories from protein was associated with increased UVOL (adj R² = 0.1370, p = 0.0340). However, protein intake was not significantly associated with fluid intake (adj R2 = 0.0569, p = 0.2516), regardless of the type of fluid consumed (p > 0.05). UOSM, USG, UCOL and the other indices of thirst were not associated with macronutrient consumption (p>0.05). CONCLUSION: These results suggest increased consumption of dietary protein intake is associated with improved hydration status based on increased 24h urine volume, with the maintenance of other urinary hydration indices. Grant or Funding Information: This study was funded by a University of North Carolina at Greensboro Office of Research Engagement New Faculty Grant. [ABSTRACT FROM AUTHOR]

PURPOSE: Appropriate fluid intake is important to offset water losses induced by physical activity. However, limited research has investigated the relationships between physical activity and fluid intake in free living individuals. To assess the associations between objective and subjective measures of physical activity, reported fluid intake, and urinary hydration biomarkers in free living emerging adults. METHODS: Thirty-four participants (44% female; age, 23±4 years; height, 172.9±10.3cm; body mass, 77.2±18.1kg; body fat, 18.4± 8.4%) provided a 24h urine sample across seven consecutive days for measures of urine volume (UVOL), urine osmolality (UOSMO), urine specific gravity (USG) and urine color (UCOL). A validated fluid log was used to record fluid intake each day, where fluid intake was segmented into morning (waking - noon), afternoon (noon - 5pm) and evening (5pm - sleep). Physical activity was objectively assessed using wrist-worn actigraphy and analyzed using standard cutoffs. Subjective assessment of physical activity quantified day-level physical activity and was measured by MET-minutes. RESULTS: Over 7 consecutive days, participants participated in 16.97±23.42 minutes of moderate-to-vigorous physical activity (MVPA) and 677.89±702.62 MET-minutes of activity per day. Mean 7-day fluid consumption was 2551±1056mL and corresponding mean 7-day UVOL, UOSMO, USG, and UCOL was 1699±865mL, 567±210 mOsm/kg, 1.020±0.010 AU, and 3.9±1.0 AU, respectively. Increased MVPA (adj R² = 0.114, p = 0.044) and increased total MET-minutes (adj R² = 0.192, p = 0.005) was associated with decreased 24 h USG. Greater morning and evening fluid consumption was associated with increased 24h UVOL (adj R² = 0.589, P = 0.02; P < 0.0001). Greater evening fluid intake was associated with lower 24h UOSMO (adj R² = 0.331, P = 0.01). CONCLUSIONS: Increasing day-level physical activity in addition to consuming a greater volume of fluids in the morning and evening improved hydration status as reflected by 24h urinary hydration variables. Further work is needed to explore the associations between physical activity and timing of the fluids consumed. Grant or Funding Information: This study was funded by a University of North Carolina at Greensboro Office of Research Engagement New Faculty Grant. [ABSTRACT FROM AUTHOR]