Automated supplement correction in passive urine color measurement device for real-time hydration testing

Abstract

Monitoring urine color as a means of assessing hydration status has long been a recommended technique for active populations like athletes, military personnel, and outdoor laborers. Urine color correlates well to urine concentration and is a simple, non-invasive practice. However, currently this approach is subjective, and errors arise in variation in ambient lighting conditions, comparator colors used, and individual perception. In addition, certain supplements such as riboflavin (vitamin B2) and beet juice are known to distort urine color and can confound hydration assessment. An automated urinalysis device (InFlow) was developed to measure urine color, an index of hydration status, in real-time during urination in the presence of these supplements. Machine learning techniques were used to reduce mean absolute hydration assessment error from riboflavin-derived color skew from 2.50 ± 0.37 to 0.85 (±0.06) color units on a 7-point color chart scale compared to a commercial colorimeter. In the absence of supplements and in the samples spiked with beet juice the InFlow device produced a mean absolute error of 0.48 (±0.06) color units. Finally, we demonstrate the feasibility of detecting myoglobinuria for potential future use in rhabdomyolysis screening. Our results show the InFlow device provides a novel approach with appropriate accuracy for standardizing hydration assessment via urinalysis in environments with high testing frequency demands in the presence of common urine color interferents including riboflavin and beet juice.