Preserving Sample, Interface, and Signal Fidelity in Wearable Sweat Electrolyte Monitoring During Exercise

Sweat electrolytes, including Na⁺, K⁺, and Cl^-, provide dynamic information on exercise-induced fluid and electrolyte regulation. Wearable ion-selective electrode (ISE) platforms now enable continuous, in situ monitoring of these ions, but performance in standard solutions does not guarantee reliable on-body data. During exercise, measurement distortion can arise before sweat contacts the electrode, at the electrochemical interface, and during signal acquisition and interpretation. This review synthesizes these coupled distortion pathways through a three-layer fidelity framework. Sample fidelity describes whether collected sweat preserves its temporal sequence, concentration state, and spatial origin during collection and microfluidic transport. Interface fidelity concerns stable ion recognition, ion-to-electron transduction, and reference-potential output in complex sweat matrices under deformation. Signal fidelity covers potential readout, transmission, calibration, temperature compensation, drift correction, and concentration estimation. By organizing recent work around these layers, we identify how sampling lag, residual-sweat mixing, evaporation, fouling, component leaching, interfacial drift, reference instability, and algorithmic overprocessing can propagate into biased electrolyte profiles. We argue that wearable sweat electrolyte monitoring should move from isolated electrode optimization toward end-to-end fidelity preservation and validation under realistic exercise conditions. This framework provides a practical basis for sensor design, performance reporting, and translation to sports, occupational safety, and digital health applications.