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Chang et al. Soft Sci. 2026, 6, 29 Page 11 of 26

For energy metabolism monitoring
The combined monitoring of lactic acid and LDH offers complementary biological clues for evaluating
muscle fatigue injury, spanning the dual dimensions of “metabolic intensity-structural damage” . Together,
[108]
these biomarkers exhibit clear synergistic value in the muscle physiological function chain.

As a key metabolite in anaerobic glycolytic, lactic acid concentration directly reflects the intensity of muscle
metabolic load and is technically amenable to continuous in vivo monitoring. It stands out as one of the most
clinically translatable wearable biochemical markers to date. By integrating the lactic acid oxidase catalytic
system with an amperometric signal readout module, researchers can precisely measure lactic acid levels.
Meanwhile, the microfluidic structure embedded within wearable patches stabilizes sweat flux and mitigating
interference from sweat rate fluctuations [109] . Importantly, such patches can be integrated with
electrophysiological detection channels (e.g., ECG), and low-power wireless transmission module, enabling
synchronous multi-signal acquisition and transmission across the “metabolism-electrophysiology” [110] .
Technical prototypes have demonstrated dynamic monitoring and wireless transmission of lactic acid
concentration in real scenarios, including sports training and high-intensity interval training, fully verifying
their potential in sports and rehabilitation applications [109] . In contrast, LDH - a key enzyme regulating the
conversion of lactic acid to pyruvate - indirectly indicates muscle cell membranes integrity through activity
changes (e.g., enzyme release caused by injury), positioning it as a potential marker of muscle structural
damage [111] . However, wearable detection of protein markers such as LDH remains hindered by technical
bottlenecks, limiting its development to the methodological exploration stage without reliable real-world
applications [112,113] .

Sweat urea and blood urea, originating from distinct body fluid chambers differ fundamentally in their
physiological information and cannot be directly equated or interchanged . They occupy unique niches in
[17]
muscle training and rehabilitation monitoring. As an end product of nitrogen metabolism, sweat urea
provides dynamic insights into individualized metabolic status (e.g., protein breakdown intensity) and
hydration levels during training and rehabilitation , making it a promising target for wearable monitoring.
[111]
Sweat urea detection relies on urease as the core biometric recognition element, converting urea
concentration into measurable signals via specific catalytic reactions, with common detection principles
including potentiometric method or amperometric method [114,115] . To support long-term monitoring, devices
typically incorporate microfluidic flow-limiting structure (to stabilize the flow of sweat samples), skin
adhesives optimized for sweaty regions (to enhance sample collection efficiency), and a low-power wireless
transmission module (for real-time data feedback), ensuring continuous recording during dynamic scenarios
such as training and recovery . Emphasizing “trend tracking” (e.g., urea concentration fluctuations across
[116]
exercise cycles) over precise quantification, sweat urea monitoring provides scenario-based guidance for
metabolic load adjustment and hydration optimization .
[109]

Conversely, blood urea detection via interstitial fluid (ISF) sampling using microneedle arrays, combined
with electrochemical or colorimetric immunoassays, shows certain potential due to the correlation between
ISF and blood urea concentrations [117] . However, this scheme still faces unresolved challenges: skin safety
concerns with microneedle puncture, device stability during prolonged wear (e.g., microneedle blockage,
signal drift), and interindividual calibration of ISF-to-blood urea concentration. These hurdles delay its
clinical-grade application in the near term .
[118]
For muscle injury monitoring
Mb and CK can form an integrated indicator pair for “acute injury-progression assessment”: Mb rises within
hours after loading and peaks earlier than CK, making it suitable for identifying early intervention timing
post-exercise [119, 120] . CK rises at 12-24 h and peaks at 24-72 h, positioning it for evaluating decision points in