Background: Previous studies have identified links between fluid intake, hydration related hormones and cortisol measured at one timepoint but have not considered how hydration may influence cortisol dynamics throughout the day. This study assessed associations between hydration status (copeptin, urinary osmolality, urine volume) and habitual fluid intake with cortisol dynamics. Methods: The day before (DB) a 6-h laboratory visit, 29 male participants (age, 23 +/- 4y; BMI, 25.5 +/- 4.3 kg/m(2); body fat, 17.3 +/- 9.3 %) provided 24-h urine samples and a fasted blood sample for hydration status assessment, recorded their 24-h fluid intake for three days prior, and provided 10 saliva samples to assess cortisol dynamics from DB into the evening of the laboratory visit. Calculated indices of cortisol dynamics included: nocturnal cortisol rise (NCR - salivary cortisol rise from bed to awakening), peak salivary cortisol (peak S-CORT - highest cortisol of all samples), cortisol awakening response (Delta CAR - difference between high morning sample and awakening sample), area under the curve with respect to ground (AUCG) and increase (AUCI), and diurnal cortisol slope (DCS - rate of change in cortisol from awakening to bed). The relationships between fluid intake or hydration status and cortisol dynamics were analyzed by separating participants into fluid intake tertile groups and by regressing cortisol dynamics on the continuous variables of total fluid intake (TFI) or hydration biomarkers. Results: There were no between-group differences for Delta CAR (p = 0.89), AUCG (p = 0.57), AUCI (p = 0.48), peak S-CORT (p = 0.14), NCR (p = 0.95), DCS (p = 0.22), or serum cortisol (p = 0.61). TFI was not associated with log (peak S-CORT) (p = 0.49), Delta CAR (p = 0.61), AUCG (p = 0.76), or AUCI (p = 0.56). Copeptin was not associated with log (peak S-CORT) (p = 0.99), Delta CAR (p = 0.22), AUCG (p = 0.69) or AUCI (p = 0.18). Urinary hydration markers were not associated with any measures of cortisol dynamics (p > 0.05). These null effects were consistent when controlling for physical activity, sleep, and body fat percentage. Conclusion: In the absence of dehydrating stimuli, measures of fluid intake or hydration status may not be associated with cortisol dynamics in young healthy males.

PurposeTo examine the associations between mask-wearing on fluid consumption and physical activity behaviors during the COVID-19 pandemic.Methods137 college students (female, 72.5%; age, 26 ± 9 y) completed a survey detailing their fluid intake, physical activity behaviors, and time spent wearing a mask throughout the day during the previous month in the Fall 2020 academic semester.ResultsIncreased daily mask wearing time was not associated with total fluid intake (p > 0.05). Participants had greater odds of being ‘somewhat active’ compared to ‘inactive’ with an increase in mask wearing time (OR = 1.23 [1.03, 1.47], p = 0.022).ConclusionWearing a mask during the COVID-19 pandemic did not influence fluid intake behaviors, however, it increased the likelihood of reported greater levels of physical activity. These factors may be related to an individual being more likely to globally adopt healthier behaviors, however, this needs further exploration.

Objective: The primary aim of this study was to assess the efficacy of the weight, urine, and thirst (WUT) framework in predicting dehydration after a body water manipulation protocol, while concurrently determining the individual and interactive contributions of the model components. Methods: The total study sample was 93 participants (female, n = 47), recruited from two institutions. Phase 1 involved collecting daily hydration measures from free-living participants (study 1, 58 participants for 3 d; study 2, 35 participants for 7 d). Phase 2 entailed a 2-h passive heating protocol, where participants from study 2 were randomly assigned to one of three groups that manipulated total body water over 24 h using passive heating and fluid restriction. During each phase, participants provided urine samples, underwent body mass measurements, and completed questionnaires pertaining to thirst perception. Morning and 24-h urine samples were assessed for color, osmolality, and specific gravity. Differences between intervention groups, based on the probability of hydration status, were examined (ANOVA), and ridge regression analysis assessed the relative importance of variables within the WUT model. Results: The study revealed significant differences among the intervention groups for predicted probability of dehydration, as determined by changes in body mass (P = 0.001), urine color (P = 0.044), and thirst perception (P < 0.001). Binomial ridge regression indicated that changes in body mass (58%) and thirst perception (26%) were the most influential predictors of dehydration. Conclusions: These data support use of an enhanced version of the WUT model, underscoring the significance of changes in body mass and thirst perception in the assessment of hydration status.

PURPOSE: Elevated morning concentrations of cortisol and copeptin are associated with poor executive function, but the influence of these hormones on cognitive function (cognitive inhibition domain [i.e., response inhibition and interference control]) following sleep in a hot environment is not known. This study examined the influence of sleep environment on cortisol and copeptin and their effect on cognitive inhibition (CI). METHODS: Ten healthy adults (female, n = 1; age, 25 ± 4y; height, 177.9 ± 7.4 cm; body mass, 75.8 ± 13.8 kg; body fat, 13.5 ± 7.1%) completed two nights of in-laboratory assessments while sleeping in temperate (TTEMP, 25 °C, 30% RH) and hot (THOT, 30 °C, 30% RH) environmental conditions. Upon awakening, blood was collected to examine serum copeptin (SCOP), and salivary samples were collected at 0, 30, and 45 minutes post-awakening, and the greatest morning cortisol concentration (CPEAK) was used. CI was assessed via the Stroop Color Word Task with inhibitory control (measured by inverse incongruency and inverse congruency) reflecting participants’ ability to resist distractions and respond faster to stimuli, indicating better cognitive performance. Paired samples t-tests evaluated between-condition differences in hormones and environmental conditions on CI. Linear mixed models assessed the effect of hormones and environmental conditions on CI. RESULTS: Hormone concentrations did not differ between conditions (mean ± SD [collapsed across conditions]), SCOP, 8.77 ± 3.13 pmol/L (p = 0.90); CPEAK, 21.83 ± 5.62 ng/mL (p = 0.86). There were no differences in CI across conditions when measured by inverse incongruency (MD [95% CI], -27.73 ms [-107.88, 52.42], p = 0.45) or inverse congruency (8.29 ms [-38.18, 54.78], p = 0.69). When controlling for environmental conditions, higher CPEAK was associated with reduced CI (β = -8.68 [95%CI, -14.71, -2.54], p = 0.02) however, there was no significant effect of SCOP on CI (p = 0.91). CONCLUSIONS: Higher CPEAK following a bout of sleep in a hot environment were associated with better incongruency scores, or an ability to display interference control and resist distractions during a CI task. Changes in environmental sleeping conditions did not affect CI the next morning, however, the effect of CPEAK on this response requires further exploration.

PURPOSE: Chronic elevations in cortisol are associated with preference for foods higher in fat and sugar content, which may increase energy intake (EI). Underhydration has been associated with greater risk for obesity and elevated resting cortisol concentrations. However, the influence of underhydration on cortisol responses and subsequent EI remains largely unknown. This study explored the effects of variations in copeptin (a marker of underhydration) and cortisol on energy and macronutrient intake during an ad-libitum breakfast. METHODS: Ten healthy adults (1 female; Age, 25 ± 4 y; Height, 177.9 ± 7.4 cm; body mass, 75.8 ± 13.8 kg; body fat, 13.5 ± 7.1%) received an ad-libitum breakfast with a variety of food items on two separate occasions the morning after sleeping in temperate (TTEMP, 25 °C, 30% RH) and hot (THOT, 30 °C, 30% RH) environmental conditions, in a randomized order. Blood and saliva were collected immediately prior to the ad-libitum breakfast (45 minutes post-awakening), analyzed for serum copeptin (COP) and salivary cortisol (CORT), respectively. Separate random-intercept linear mixed-effects models assessed the effects of individual mean-centered COP and CORT concentrations on ad-libitum EI and macronutrient intake at breakfast, controlling for condition. RESULTS: CORT concentration (p = 0.84), COP concentration (p = 0.84), EI (p = 0.25), and protein (p = 0.62), fat (p = 0.53), and carbohydrate (p = 0.239) consumption did not differ by condition. Within-person changes in copeptin or cortisol were not associated with EI (kcals, βCOP = 20.38, [95%CI; -35.93, 76.79], p = 0.463], βCORT = -14.02, [-33.68, 5.64], p = 0.17) or macronutrient intake; (protein, βCOP = -0.15 [-2.74, 2.45], p = 0.91, βCORT = -0.37, [-1.28, 0.53], p = 0.41; carbohydrates, βCOP = 4.90 [-4.25, 14.0] p = 0.29, βCORT = -1.34, [-4.53, 1.86], p = 0.398; fat, βCOP = 0.24 [-3.70, 4.19], p = 0.90, βCORT = -0.78, [-2.16, 0.59], p = 0.26) when controlling for condition. CONCLUSIONS: Variations in morning CORT and COP did not influence ad-libitum EI or macronutrient intake at breakfast. These preliminary findings suggest that variations in these markers may not influence EI, but greater perturbances to these hormones and the effect on eating behaviors warrants further consideration. Funding Information: This study was funded in part by Bedgear, LLC.

PURPOSE:This study determined fluid intake and physical activity behaviors among college students during the COVID-19 pandemic. METHODS:College students (n = 1014; females, 75.6%) completed an online survey during the Spring 2020 academic semester following the initial global response to the COVID-19 pandemic. Academic standing, habitation situation, and University/College responses to COVID-19 were collected. Participants completed the Godin Leisure-Time Exercise Questionnaire and a 15-item Beverage Questionnaire (BEVQ-15) to determine physical activity level and fluid intake behaviors, respectively. RESULTS:Females (1920 ± 960 mL) consumed significantly less fluid than males (2400 ± 1270 mL, p < 0.001). Living off-campus (p < 0.01) and living with a spouse/partner (p < 0.01) was associated with increased consumption of alcoholic beverages. 88.7% of participants reported being at least moderately active; however, Black/African American and Asian participants were more likely to be less active than their Caucasian/White counterparts (p < 0.05). Participants reporting no change in habitation in response to COVID-19 had a higher fluid intake (p = 0.002); however, the plain water consumption remained consistent (p = 0.116). While there was no effect of habitation or suspension of classes on physical activity levels (p > 0.05), greater self-reported physical activity was associated with greater fluid intake (std. β = 0.091, p = 0.003). CONCLUSIONS:Fluid intake among college students during the initial response to the COVID-19 pandemic approximated current daily fluid intake recommendations. Associations between COVID-19-related disruptions (i.e., suspension of classes and changes in habitation) and increased alcohol intake are concerning and may suggest the need for the development of targeted strategies and programming to attenuate the execution of negative health-related behaviors in college students.

PURPOSE: Sleep quality and quantity may be impacted by environmental conditions. These disruptions to sleep may also impact energy intake (EI). However, the effect of room temperature on sleep outcomes and next morning EI has not been examined. This study examined the impact of sleeping in a temperate (TTEMP) or hot (THOT) environment on objective measures of sleep quality and quantity, and ad-libitum EI and macronutrient intake the next morning. METHODS: Ten healthy adults (female n = 1; age, 25 ± 4 y; height, 177.9 ± 7.4 cm; body mass, 75.8 ± 13.8 kg; body fat, 13.5 ± 7.1%) completed two overnight trials in an environmental chamber set to 25 °C, 30% RH (TTEMP) and 30 °C, 30% RH (THOT). Sleep outcomes (e.g., sleep architecture, total sleep time, efficiency, wake after sleep onset) were measured with polysomnography. Participants self-selected food items to consume ad libitum during breakfast. Paired samples t-tests evaluated between-trial differences in sleep outcomes and EI. Linear mixed models examined the effects of trial and sleep architecture on breakfast EI and macronutrient intake. Delta values between conditions (THOT - TTEMP) were calculated to explore associations between delta sleep architecture with delta EI and macronutrient intake. RESULTS: Percent of time spent in stage 3 sleep was significantly lower in THOT than TTEMP (MD: - 2.51% [95%CI; -4.84, -0.18], p = 0.04). There were no differences in total sleep time, sleep efficiency, wake after sleep onset, or other sleep stages (REM, stage 1, and stage 2) between conditions (p > 0.05). There was no significant difference in ad-libitum EI or macronutrient intake between conditions (p > 0.05). No sleep outcomes were significantly associated with EI and macronutrient intake between trials, nor were delta sleep stage durations significantly associated with delta EI and macronutrient intake (p > 0.05). CONCLUSIONS: The relative time spent in stage 3 sleep was significantly lower when sleeping in THOT than in TTEMP, suggesting that hot environmental conditions may lead to poorer sleep quality via less slow wave/restorative sleep. However, this difference in relative stage 3 sleep, or other sleep outcomes, was not associated with EI and macronutrient intake the next morning, which were also similar between conditions. FUNDING: This study was funded in part by Bedgear, LLC.

The timing of puberty and menarche (early versus late) have been shown to influence BMI in females, yet less is known about body composition (FMI and FFMI, respectively) related to these events. PURPOSE: To assess the influence of early versus late puberty and menarche on FMI and FFMI in females. METHODS: As part of a larger longitudinal study, BMI (lab-based height and weight), body composition and pubertal variables (pubertal development scale; PDS) were collected from females (N = 98). At age 10, mothers reported pubertal development (PDS; Range 1-4;1-2 = late puberty (N = 50), 3-4 = early puberty (N = 48)). At age 15 girls self-reported age at menarche (median split for early versus late menarche). BMI and body composition were measured in early emerging adulthood (21.1 ± 0.68 years) and FMI and FFMI for pubertal or menarche groups were compared across BMI groups (<.5 SD, ±.5 SD or > .5 SD of puberty or menarche group means for low [LBMI], mean [MBMI] or high [HBMI] BMI groups, respectively) using two-way MANOVAs with interaction terms. Post hoc univariate tests with Tukey post-hoc adjustments were run to probe significant interaction terms. RESULTS: Age 10 BMI was higher in girls with early puberty than those with late puberty (23.3 vs 18.2 kg/m2; p < .001), but age 10 BMI was similar when girls were stratified by early versus late menarche (21.0 vs 20.2 kg/m2, respectively). There was a significant interaction between pubertal development and BMI on FMI and FFMI (Λ = .599, p = 0.002), with early pubertal timing associated with higher FMI than late pubertal timing in the MBMI (p = .012) and HBMI (p < .001) groups but not in LBMI (p = .96). There was no interaction effect in the univariate test for pubertal timing and BMI on FFMI or for menarche timing and BMI on FMI and FFMI (Λ = .938, p = 0.276). CONCLUSIONS: In emerging adulthood, body composition in females was associated with early pubertal timing but not with early menarche, suggesting that differences in FMI and FFMI during this time are influenced largely by the age of onset of puberty and less by age of menarche. While timing of puberty may be a critical contributor to fat mass, further research is needed to delineate the role of early childhood body composition on pubertal versus menarche timing and the ability of physical activity to alter adolescent body composition trajectories.

PURPOSE: This study investigated the associations of total water intake (TWI) on stress, mood, sleep, and activity across the menstrual cycle (MC). METHODS: Seventeen naturally cycling females (mean ± SD; age, 24 ± 5 y; mass, 71.3 ± 24.4 kg; height, 163 ± 7 cm; body fat, 22.7 ± 9.3%) visited the laboratory on three consecutive days during three phases of their MC; early follicular (EF, days 3 – 5), late follicular (LF, days 11 – 13) and mid-luteal (ML, days 18 – 20). Daily, participants recorded their TWI and wore an accelerometer on their wrist to quantify sleep efficiency (SE), sleep time (ST) and activity counts (AC). On days 5, 13, and 20, participants completed the perceived stress scale (PSS) and profile of mood states (POMS). Linear mixed effects models assessed the influence of MC phase (EF as reference) and TWI on main outcomes. RESULTS: TWI did not differ between EF (3472 ± 1505 mL), LF (3415 ± 1410 mL), or ML (3331 ± 1703 mL) phases of the MC (p = 0.172). There was no significant interaction between TWI and MC phase on PSS (LF, β = 0.0009, p = 0.096; ML, β = 0.000, p = 0.557), POMS (LF, β = 0.0024, p = 0.562; ML, β = 0.385, p = 0.979), SE (LF, β = -0.002, p = 0.761; ML, β = -0.0007, p = 0.473), ST (LF, β = -0.0163, p = 0.163; ML, β = -0.003, p = 0.799), or AC (LF, β = -29.33, p = 0.096; ML, β = 11.33, p = 0.532). Partitioning of TWI variance into between and within subjects highlighted a significant interaction between the effect of MC and TWI on AC. Within ML relative to EF, greater TWI compared to one’s usual intake was associated with greater AC (β = 116.52 [95% CI; 31.37, 199.54], p = 0.0095), but this interaction was not observed when comparing LF to EF (β = 38.73 [-50.866, 128.26], p = 0.413). Independent of MC phase, greater TWI was associated with greater AC (β = 39.37 [13.283, 65.28], p = 0.00421). CONCLUSIONS: Across the menstrual cycle, total water intake was not associated with differences in stress, mood, or sleep. Within participants, an increase in total water intake (compared to habitual intake) was associated with greater activity counts, with more activity being performed in the mid-luteal phase of the menstrual cycle. Since mean total water intake in this study exceeded daily adequate intake recommendations, elucidating these outcomes in underhydrated females across the menstrual cycle should be explored.

Abstract Objectives Acute water ingestion before a meal is suggested to reduce energy intake (EI) by promoting satiety; however, the influence of daily fluid intake on associated EI has yet to be extensively explored. This study determined the relationship between habitual total fluid intake and EI in emerging adults. Methods 54 free-living college students (45% female; age, 23 ± 4 years; height, 173.5 ± 12.2 cm; body mass, 75.8 ± 17.6 kg; body fat (BF), 19.0 ± 8.7%) provided a 24 h urine sample across 7 consecutive days and recorded their daily food and fluid intake. Daily perceptual measures of thirst were assessed in a subset of participants (n = 34) using separate 100mm visual analog scales to assess perceived thirstiness, pleasantness (mouth), dryness (mouth), taste (mouth), fullness (gastrointestinal tract), and sickness (gastrointestinal tract). Linear mixed effect models with random intercepts assessed the associations of between and within-person changes in daily fluid intake (total fluid (TF), plain water intake (PW), and total water intake from food and fluids (TWI)), BF, and thirst ratings on EI via person-mean centering. Results On average, participants consumed 2626 ± 1357 ml TF, 1812 ± 1276 ml PW, 3049 ± 1441 ml TWI, and 1950 ± 717 kcals across all observations. Participants that consumed more PW than the group mean had greater total EI above the group mean (β = 0.156 [0.03, 0.28], p = 0.015). However, when participants consumed more PW than is typical, they reported a lower total EI compared to their individual mean (β = −0.15, [−4.7e-3, −0.29], p = 0.0468). Between participants, greater ratings of thirst were associated with lower EI (β = −9.22, [−17.26, −1.20], p = 0.032), however, within-person increases in thirst were associated with greater EI (β = 11.14, [2.01, 20.27], p = 0.021). When covarying for TWI, individuals with higher BF reported lower total EI (β = −17.48 [−31.87, −3.12], p = 0.022). Conclusions Increasing PW intake above one's typical volume can potentially reduce EI, perhaps through mechanisms or perceptions of increased satiety. Disparate findings for between and within-person effects of PW on EI warrant further investigation into other variables influencing EI and PW intake such as physical activity and food preferences. Funding Sources This study was funded in part by a grant from the Office of Research and Engagement at the University of North Carolina at Greensboro.

BACKGROUND:Black adults experience higher levels of stress and more dysfunctional sleep patterns compared to their White peers, both of which may contribute to racial disparities in chronic health conditions. Dysfunctional sleep patterns are also more likely in emerging adults compared to other age groups. Daily stress-sleep relations in Black emerging adults are understudied.PURPOSE:This study used ecological momentary assessment (EMA) and wrist-worn actigraphy to examine bidirectional associations between daily stress and sleep among Black emerging adults.METHODS:Black college freshmen (N = 50) completed an EMA protocol (i.e., five EMA prompts/day) and wore an accelerometer for 7 days. The first EMA prompt of each day assessed sleep duration and quality. All EMA prompts assessed stress. Wrist-worn actigraphy assessed nocturnal sleep duration, sleep onset latency, sleep efficiency, and waking after sleep onset.RESULTS:At the within-person level, stress experienced on a given day was not associated with any sleep metrics that night (p > .05). On evenings when actigraphy-based sleep duration was shorter (B = -0.02, p = .01) and self-reported sleep quality was poorer (B = -0.12, p = .02) than usual, stress was greater the following day. At the between-person level, negative bidirectional relations existed between stress and actigraphy-based waking after sleep onset (stress predicting sleep: B = -0.35, p = .02; sleep predicting stress: B = -0.27, p = .04).CONCLUSIONS:Among Black emerging adults, associations between daily sleep and stress vary at the between- and within-person level and are dependent upon the sleep metric assessed. Future research should compare these relations across different measures of stress and different racial/ethnic groups to better understand health disparities.

Variations in female sex hormones across the menstrual cycle (MC) influence the osmotic threshold for release of the hormone arginine vasopressin, which alters fluid retention and thirst perception. However, fluid intake behaviors across the MC have yet to be explored. PURPOSE: To determine differences in fluid intake behaviors and hydration status across the menstrual cycle in naturally cycling females. METHODS: Seventeen naturally cycling females (mean ± SD; age, 24 ± 5 y; height, 163 ± 7 cm; mass, 71.3 ± 24.4 kg; body fat, 22.7 ± 9.3%) provided 24 h urine samples over three consecutive days during three timepoints throughout their MC; early follicular (MCEF, days 3-5), late follicular (MCLF, days 11-13), and mid-luteal (MCML, days 18-20) phases, where day 0 was defined as the start of menstruation. Participants also completed daily fluid and dietary intake logs to assess fluid intake. Urinary hydration biomarkers assessed included urine volume (UVOL), urine osmolality (UOSM), urine specific gravity (USG), and urine color (UCOL). MC phase was used in separate linear mixed effects models as a fixed effect predictor of total fluid intake (TFI, all fluids from beverages and foods) and urinary hydration biomarkers, with a random effect of participant. RESULTS: There was no significant effect of MC phase on TFI (MCEF, 3472 ± 1505 mL; MCLF, 3416 ± 1410 mL; MCML, 3331 ± 1703 mL; β [95% CI], -171.59 [-348.7, 6.8], p = 0.172). UVOL was significantly greater during MCEF (2.36 ± 1.33 L) compared to MCLF (2.09 ± 1.41 L) and MCML (2.20 ± 1.40 L), β = -0.145 [-0.268, -0.022], p = 0.022. UOSM was significantly greater during MCML (465 ± 217 mOsm·kg-1) than MCEF (393 ± 193 mOsm·kg-1) or MCLF (426 ± 203 mOsm·kg-1), (β = 44.90 [19.0, 70.8], p = 0.001). However, there was no impact of MC phase on UCOL (β = 0.064 [-0.12, 024], p = 0.489) or USG (β = 0.0003 [-0.001, 0.002], p = 0.751). CONCLUSION: Our results indicate fluid intake behaviors do not change across the menstrual cycle in naturally cycling females despite differences in 24 h urine volume and osmolality. Since mean total fluid intake exceeded daily adequate intake recommendations and 24 h urinary hydration markers indicated euhydration, the clinical implications of menstrual cycle phase on hydration status may be minimal, but this remains to be fully elucidated in women not meeting fluid intake guidelines.

This study determined the beverage hydration index (BHI) and postprandial cardiac autonomic activity after consuming an isotonic beverage (IB) compared to distilled water (DW). Twenty-two participants (50% female; mean ± SD; age, 27 ± 3 year; height, 169.1 ± 12.6 cm; weight, 73.3 ± 13.8 kg; BF%, 23 ± 10%) completed two experimental trials where they consumed 1 L DW or an IB; after which urine volume and cardiac autonomic activity was measured through 240 min. Cardiac autonomic activity was quantified using heart rate (HR), log transformed heart rate variability measures (root mean square of successive R–R intervals; RMSSD; low frequency, LF; and high frequency, HF) and systolic time intervals (pre-ejection period, PEP). BHI was significantly greater after IB consumption at min 0 (MD [95% CI]; 1.31 [0.35, 2.27]), 180 min (0.09 [0.022, 0.16]), and 240 min (0.1 [0.03, 0.17]) compared to DW (p = 0.031). Net fluid balance was significantly greater in IB than DW at 180 min (90 [−16.80, 196.81]) and 240 min (106 [−13.88, 225.88]) (p = 0.037). HR decreased over time in both beverage trials but was higher following IB ingestion at 0 min (3.9 [−2.42, 10.22]), 30 min (5.3 [−0.94, 11.54]), and 60 min (2.7 [−3.42, 8.82]) (p = 0.0002). lnHF was greater 30 min post DW ingestion compared to IB (0.45 [−0.23, 1.13]) (p = 0.039). IB promotes greater fluid retention capacity compared to DW within 4 hours of consumption. The variations in cardiac autonomic measures may warrant further investigation in clinical populations (i.e., patients with autonomic failure).

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 ± 10.3 cm; body mass: 77.2 ± 18.1 kg; body fat: 18.4 ± 8.4%) recorded their food and fluid intake, and provided a 24 h 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 100 mm 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 ± 1122 mL fluid per day. Mean 7-day 24 h urinary hydration markers were UVOL:1724 ± 850 mL, 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 24 h 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.

Limited evidence exists examining the manipulation of total body water on inflammatory biomarkers in free-living emerging adults. PURPOSE: The purpose of this study was to investigate the acute effects of partial and full fluid restriction on inflammatory biomarkers in males and females. METHODS: Thirty-one participants (42% female; age, 23 ± 4 y; mass, 78.2 ± 17.3 kg; height, 173.2 ± 9.9 cm; body fat, 18.2 ± 8.7%) provided a 24 h measure of urine volume (UVOL) and urine osmolality (UOSM), 24 h dietary and fluid intake record, and fasted blood draw for two consecutive days. Following measure of nude body mass (NBM) and blood draw on day 1, participants underwent a 2 h sauna exposure (41 °C, 50% RH) and were randomly assigned to one of three groups; control (CON) where females and males consumed 2.0 and 2.5 L of water, respectively, partial fluid restriction (PART) where water consumption matched sauna sweat losses, and full fluid restriction (FULL) where participants were restricted from consuming fluids prior to day 2. Blood samples were analyzed for c-reactive protein (CRP), interleukin 6 (IL-6), interleukin 8 (IL-8), interleukin 10 (IL-10), interferon gamma (IFN-γ), tumor necrosis factor alpha (TNF-α) and copeptin, a surrogate for arginine vasopressin. RESULTS: On day 2, percent body mass loss in PART (MD [95%CI]; 1.2% [0.2, 2.1], p = 0.013) and FULL (1.4% [0.45, 2.5], p = 0.004) were significantly greater than CON (0.0 ± 0.5%). Higher UOSM (p = 0.001) and copeptin (p < 0.001), and lower UVOL(p = 0.008) were observed in PART and FULL compared to CON. CRP was significantly lower in CON (estimate [95%CI]; -7.35 pg/mL [-4.81, -13.13], p = 0.011) than PART, however there were no differences between CON (1.82 ± 1.55 pg/mL) and FULL (3.93 ± 6.17 pg/mL, p > 005). IL-8 was significantly greater in FULL (estimate, 3.12 pg/mL [0.49, 5.72], p = 0.021) than PART. There were no differences in IL-6, IL-10, IFN-γ, or TNF-α between groups (p > 0.05). CONCLUSIONS: Partial and full fluid restriction over 24 h produced significant changes in circulating concentrations of CRP and IL-8, however, these differences were not consistent between hydration groups. Twenty-four-hour changes in total fluid intake eliciting mild hypohydration (<1.5% body mass loss) may be insufficient to cause a sustained systemic inflammatory response in free-living emerging adults.

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 ± 10.3 cm; body mass, 77.2 ± 18.1 kg; body fat, 18.4 ± 8.4%) provided a 24 h 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 - 5 pm) and evening (5 pm - 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 ± 1056 mL and corresponding mean 7-day UVOL, UOSMO, USG, and UCOL was 1699 ± 865 mL, 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 24 h UVOL (adj R2 = 0.589, P = 0.02; P < 0.0001). Greater evening fluid intake was associated with lower 24 h 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 24 h 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.

Context: Cold-water immersion (CWI) may not be feasible in some remote settings, prompting the identification of alternative cooling methods as adjunct treatment modalities for exertional heat stroke (EHS). Objective: To determine the differences in cooling capacities between CWI and the inhalation of cooled air. Design: Randomized controlled clinical trial. Setting: Laboratory. Patients or Other Participants: A total of 12 recreationally active participants (7 men, 5 women; age = 26 +/- 4 years, height = 170.6 +/- 10.1 cm, mass = 76.0 +/- 18.0 kg, body fat = 18.5% +/- 9.7%, peak oxygen uptake = 42.7 +/- 8.9 mL.kg(-1).min(-1)). Intervention(s): After exercise in a hot environment (40 degrees C and 40% relative humidity), participants were randomized to 3 cooling conditions: cooling during passive rest (PASS; control), CWI, and the Polar Breeze thermal rehabilitation machine (PB) with which participants inspired cooled air (22.2 degrees C +/- 1.0 degrees C). Main Outcome Measure(s): Rectal temperature (T-REC) and heart rate were continuously measured throughout cooling until T-REC reached 38.25 degrees C. Results: Cooling rates during CWI (0.18 degrees C.min(-1) +/- 0.06 degrees C.min(-1)) were greater than those during PASS (mean difference [95% CI] of 0.16 degrees C.min(-1) [0.13 degrees C.min(-1), 0.19 degrees C.min(-1)]; P < .001) and PB (0.15 degrees C.min(-1) [0.12 degrees C.min(-1), 0.16 degrees C.min(-1)]; P < .001). Elapsed time to reach a T-REC of 38.25 degrees C was also faster with CWI (9.71 +/- 3.30 minutes) than PASS (-58.1 minutes [-77.1, -39.9 minutes]; P < .001) and PB (-46.8 minutes [-65.5, -28.2 minutes]; P < .001). Differences in cooling rates and time to reach a T-REC of 38.25 degrees C between PASS and PB were not different (P > .05). Conclusions: Transpulmonary cooling via cooled-air inhalation did not promote an optimal cooling rate (>0.15 degrees C.min(-1)) for the successful treatment of EHS. In remote settings where EHS is a risk, access and use of treatment methods via CWI or cold-water dousing are imperative to ensuring survival.