Abstract Hydration status is a critical determinant of physiological function, cognitive performance, and long‑term health. Traditional markers such as urine osmolality, plasma osmolality, and body‑weight change remain the clinical gold standards, yet they suffer from limited temporal resolution, invasiveness, or susceptibility to confounding factors. Recent advances in multi‑omics profiling, wearable biosensing, and machine‑learning algorithms are converging to enable continuous, non‑invasive, and context‑aware hydration assessment. This review highlights breakthroughs in salivary and sweat biomarkers, bioimpedance‑based wearable systems, and interpretable artificial intelligence models that translate multivariate physiological signals into actionable hydration metrics. Emerging evidence also underscores the role of hydration in chronic disease prevention, athletic performance, and cognitive resilience. Future directions include the integration of real‑time hydration data with digital twin frameworks, personalised fluid‑replacement algorithms, and validation in diverse real‑world populations.

1. Introduction Maintaining euhydration is essential for thermoregulation, cardiovascular stability, renal function, and neural efficiency. Dehydration, even at 1–2% of body mass, impairs aerobic performance, mood, and attention (Cheuvront & Kenefick, 2014). Conversely, overhydration can lead to hyponatremia, particularly in endurance athletes and clinical patients. Despite decades of research, a single “gold standard” for hydration status remains elusive because body water is distributed across intracellular, extracellular, and intravascular compartments, each with distinct regulatory mechanisms. The past five years have witnessed a paradigm shift from static laboratory measurements to dynamic, personalised monitoring. This article synthesises the latest findings and technological innovations that promise to redefine how hydration is measured and managed.

2. Multi‑omics perspectives: saliva, sweat, and urine beyond osmolality While urine specific gravity and plasma osmolality remain reliable, their lag time and dependence on renal function limit utility in rapidly changing scenarios. Recent proteomic and metabolomic studies have identified novel salivary biomarkers that respond to acute dehydration. For instance, Walsh et al. (2022) reported that salivary α‑amylase activity increases by 35% after 90 minutes of exercise‑induced hypohydration, correlating with plasma osmolality (r = 0.72). Similarly, sweat‑based analysis has advanced through microfluidic patches that capture chloride, sodium, and lactate concentrations in real time. Nyein et al. (2023) demonstrated a flexible sweat sensor capable of tracking dynamic changes in sweat osmolality during graded exercise, with a response time of <5 minutes. These non‑invasive approaches offer granular, compartment‑specific insights that traditional metrics cannot provide.

3. Wearable bioimpedance and near‑infrared spectroscopy Bioelectrical impedance analysis (BIA) has been miniaturised into wearable form factors. A breakthrough by Marra et al. (2024) introduced a wrist‑worn multi‑frequency bioimpedance device that continuously measures phase angle and impedance at 5, 50, and 100 kHz. In a cohort of 60 athletes, the device tracked total body water changes with a mean absolute error of 0.8% compared to deuterium dilution. Near‑infrared spectroscopy (NIRS) of the forearm and calf has also been repurposed for hydration assessment. Matsuda et al. (2023) showed that tissue oxygenation index (TOI) declines linearly with progressive dehydration (r² = 0.89), providing a proxy for intravascular volume contraction. The combination of BIA and NIRS in a single wearable enables separation of extracellular and intracellular water shifts, a distinction critical for diagnosing hypervolemia versus cellular dehydration.

4. Machine learning for predictive hydration models The multivariate nature of hydration—influenced by ambient temperature, exercise intensity, sweat rate, renal concentrating ability, and individual genetics—demands computational integration. Deep learning models trained on continuous heart rate, skin temperature, accelerometry, and galvanic skin response can now classify hydration states with >90% accuracy (Buller et al., 2023). A particularly promising approach is the use of explainable AI (XAI) to identify which sensor features most strongly predict impending dehydration. For example, a gradient‑boosted tree model developed by Smith et al. (2024) ranked heart‑rate variability (HRV) low‑frequency power and skin‑temperature gradient as the top two predictors, enabling the creation of a simplified “hydration index” that can be deployed on low‑power microcontrollers. Such models are being validated in military, occupational, and clinical settings.

5. Clinical and performance implications Beyond athletic monitoring, hydration status is increasingly linked to chronic disease outcomes. A large prospective study by Chang et al. (2023) found that individuals with habitually low water intake (≤1.2 L/day) had a 42% higher risk of developing chronic kidney disease over 10 years, independent of estimated glomerular filtration rate. In older adults, dehydration is a major contributor to falls, delirium, and hospital readmission. Wearable hydration monitors are now being trialled in nursing homes to trigger early fluid intake reminders. Meanwhile, in elite sport, personalised hydration plans based on real‑time sweat sodium concentration have been shown to reduce cramping incidence by 60% (Baker et al., 2024). These applications highlight the translational value of moving from population‑based guidelines to individualised, data‑driven hydration strategies.

6. Future outlook: digital twins and closed‑loop rehydration The next frontier is the development of a “hydration digital twin”—a computational model that integrates continuous sensor data, individual anthropometrics, metabolic rate, and environmental inputs to simulate fluid compartment dynamics in real time. Early prototypes using physiologically based pharmacokinetic (PBPK) modelling have been demonstrated in laboratory settings, but widespread adoption requires robust validation across age, sex, and disease states. Additionally, closed‑loop systems that combine hydration sensing with automated fluid delivery (e.g., via smart bottles or intravenous pumps) are being explored for critical care and spaceflight. Ethical considerations around data privacy and the risk of over‑reliance on technology must also be addressed. Finally, standardisation of sensor calibration and the establishment of reference ranges for wearable‑derived hydration metrics remain essential for regulatory approval and clinical acceptance.

7. Conclusion Advances in hydration status assessment are being driven by the convergence of biomarker discovery, wearable microelectronics, and artificial intelligence. Salivary and sweat sensors offer non‑invasive, compartment‑specific data, while multi‑frequency bioimpedance and NIRS provide continuous estimates of fluid distribution. Machine‑learning models synthesise these streams into actionable predictions, enabling personalised hydration management. As these technologies mature, they hold promise for preventing dehydration‑related morbidity, optimising human performance, and informing public health guidelines. The challenge ahead lies in rigorous field validation, interoperability of devices, and equitable access to precision hydration tools.

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