The accuracy of a 4-item hydration self-assessment model to classify urine concentration using different cut-offs

Hydration is important for health and performance, but accurate self-assessment methods for active populations are limited. The study objective was to determine a model to classify by self-assessment a low vs. high 24-hour urine concentration, and to determine the accuracy of morning vs. afternoon assessments. The study describes the development and validation of a self-assessment model through exploratory modeling, using stepwise logistic regression. The aim was to determine which hydration markers predict urine specific gravity (using two different USG cut-offs: low ≤ 1.012, and high ≥ 1.020), in two intentionally overlapping 24-hour urine collections leading up to a morning and afternoon hydration self-assessment, and to assess its diagnostic accuracy based on the area under the curve (AUC). A total of of n = 62 wildland firefighters (WLFFs), and n = 23 recreational athletes (12% female, median age 25 years, with interquartile range of 24–32) were included. The median USG values of the 24-hour urine collections leading up to the morning and afternoon assessments, were 1.011 and 1.012, respectively. The AUC for the final model, which included self-reported fluid intake; urine frequency, volume, and color, to classify low vs. high USG, for low cut-off (≤ 1.012) was fair in the morning (0.72), and good in the afternoon (0.85), and for high cut-off (≥ 1.020) good for morning (0.81) and afternoon (0.86), respectively. This 4-item hydration self-assessment model classifies a low vs. high USG acceptable, with slightly higher accuracy in the afternoon. This insight may inform targeted hydration monitoring strategies in occupational settings, for example among WLFFs.