UC San Diego

Precision management of Parkinson's disease (PD) requires frequent levodopa (L-dopa) dose adjustments, yet current monitoring relies on subjective symptom reporting and infrequent blood testing. Here, we present a soft, fingertip-mounted wearable platform for continuous, noninvasive L-dopa monitoring. By combining osmotically harvested passive sweat with soft hydrogels, a potentiometric sensing strategy, and individualized calibration, the platform estimates blood L-dopa information from sweat without external power or iontophoresis. Strong correlations between sweat and high-performance liquid chromatography (HPLC)-measured blood L-dopa concentrations were observed in healthy ([Formula: see text]) and PD subjects ([Formula: see text]) following a single immediate-release L-dopa/carbidopa dose. Low motor symptom scores aligned with peak L-dopa levels, confirming pharmacodynamic relevance. L-dopa cleared faster in PD patients despite similar bioavailability to healthy subjects, while recorded hemodynamic responses showed short hypotensive trends for both groups. Machine learning identified sweat and blood pressure as key contributors toward accurate estimation of blood L-dopa levels (mean absolute error = 2.02 µM vs. ground truth). Overall, our easy-to-use, energy-efficient wearable supports real-time, stimulation-free monitoring, potentially enabling at-home dosage adjustments and paving the way for future autonomous closed-loop L-dopa therapeutic system development.