2. Materials and Methods
Dehydration, GI distress, dehydration, mass loss, ATAGOAll
DEHYDRATION, REGRESSION, DEHYDRATION
16 male and female university students between 18 and 35 years old were evaluated. They were apparently healthy, per the Physical Activity Readiness Questionnaire [Participants came to the laboratory after fasting for ten hours or more. A urine sample was obtained to verify urine specific gravity (USG) with a manual refractometer (ATAGOAll participants exercised under all three conditions, in random order, a minimum of two days apart: no drink (ND), drinking water ad libitum (W), or drinking lactose-free, skimmed milk ad libitum (372 kJ—89 kcal, 8 g protein, 0.5 g fat, and 98 mg sodium in 250 mL) (SM). They pedaled a stationary bike (Schwinn AC Performance Plus, Vancouver, WA, USA) at 60 to 75% HRmax for 90 min. Maximum heart rate (Hrmax) was calculated according to Tanaka, et al. [Dehydration was calculated from body mass loss during the exercise period as follows:Sweat rate was calculated from body mass loss in grams during the exercise period, as well as urine output and fluid intake in mL, according to the formula:Net Fluid Balance was calculated for each time point, using body mass immediately before exercise as the reference, as follows:This is a randomized, cross-sectional study. Descriptive statistics (mean, standard deviation, and range) were calculated for age, height, and weight to characterize the sample. All variables were checked for normality. To verify that circumstances were the same for all conditions, one-way ANOVAs were performed for the baseline measurements (BM, USG) and for the average exercise intensity (HR, RPE), dry bulb temperature, and relative humidity during the exercise sessions. Dependent variables were analyzed as follows: voluntary fluid intake was compared only between milk and water with a one-way ANOVA. Urine output (UO) and dehydration (%DEHY) were compared among all three conditions with one-way ANOVAs. Net Fluid Balance (NFB) was analyzed first with a two-way, repeated-measures ANOVA to explore the relevance of both quadratic and linear functions of the variables of interest. After verification, a regression model with the subjects included as blocks; and time and condition, as well as their interaction as potential predictors, was evaluated. NFB was the dependent variable.GI distress was analyzed in two different ways. First, scores for each symptom were analyzed with a two-way, repeated-measures ANOVA (3 conditions × 4 time points: 0, 30, 60, and 90 min post-exercise). In addition, symptom scores were transformed to nominal data, assigning a 0 for “absent” and a 1 for “present”, regardless of the time or the intensity, for each participant and each condition. This information was used to perform a logistic regression on each of the 19 symptoms. All analyses were performed with JMP