Noninvasive Estimation of Hydration Status in Athletes Using Wearable Sensors and a Data-Driven Approach Based on Orthostatic Changes

Abstract

Dehydration beyond 2% bodyweight loss should be monitored to reduce the risk of heat-related injuries during exercise. However, assessments of hydration in athletic settings can be limited in their accuracy and accessibility. In this study, we sought to develop a data-driven noninvasive approach to measure hydration status, leveraging wearable sensors and normal orthostatic movements. Twenty participants (10 males, 25.0 ± 6.6 years; 10 females, 27.8 ± 4.3 years) completed two exercise sessions in a heated environment: one session was completed without fluid replacement. Before and after exercise, participants performed 12 postural movements that varied in length (up to 2 min). Logistic regression models were trained to estimate dehydration status given their heart rate responses to these postural movements. The area under the receiver operating characteristic curve (AUROC) was used to parameterize the model’s discriminative ability. Models achieved an AUROC of 0.79 (IQR: 0.75, 0.91) when discriminating 2% bodyweight loss. The AUROC for the longer supine-to-stand postural movements and shorter toe-touches were similar (0.89, IQR: 0.89, 1.00). Shorter orthostatic tests achieved similar accuracy to clinical tests. The findings suggest that data from wearable sensors can be used to accurately estimate mild dehydration in athletes. In practice, this method may provide an additional measurement for early intervention of severe dehydration.

Recent Advancements in Wearable Hydration Monitoring Technologies: A Scoping Review of Sensors, Trends, and Future Directions (Preprint)

“…Our focus extends to populations at an elevated risk of dehydration, such as athletes, military personnel in extreme environments, individuals involved in infant and maternal health, and the elderly, detailing why these groups are particularly susceptible (21). This is because dehydration that exceeds 2% bodyweight loss may lead to heart-related injury risk (22) We will delve into the medical and operational causes and consequences of dehydration in these groups, examining both established and emerging monitoring techniques. This analysis is informed by recent advancements in wireless body sensor networks, as highlighted in studies like (23,24), and pays special attention to technologies that enable real-time monitoring, emphasizing their critical role in timely health intervention and preventive care.…”

A Non-Invasive Hydration Monitoring Technique Using Microwave Transmission and Data-Driven Approaches

“…Several recent studies have exploited data-driven and statistical machine learning-based methods for estimation of hydration status. The relationship between hydration and cardiovascular responses to orthostatic changes is leveraged to assess hydration status by framing the task as a binary classification problem [ 12 ]. Alvaraz et al employ Support Vector Machines (SVM) and k-means to implement a three-stage dehydration protocol for athletes using electrocardiograph signals [ 13 ].…”