Short Report: Estimating Blood Lactate Dynamics from Sweat Lactate and Sweat Rate After High-Intensity Exercise - A...

Area of Science:

• Sports Science
• Exercise Physiology
• Biomarker Monitoring

Background:

• Blood lactate (BL) is a key indicator of anaerobic metabolism and fatigue.
• Non-invasive alternatives like sweat lactate (SWL) and sweat rate (SWR) are being explored.
• The ability of SWL and SWR to reflect BL changes after intense exercise is not well understood.

Purpose of the Study:

• To assess if BL dynamics can be predicted using a regression model.
• To utilize time-series data of SWL and SWR from wearable sensors.
• To evaluate non-invasive methods for monitoring exercise-induced physiological changes.

Main Methods:

• Five male athletes completed a 30-second Wingate anaerobic test.
• Continuous monitoring of SWL and SWR using wearable sensors.
• Capillary blood lactate levels were measured for 30 minutes post-exercise.

Main Results:

• Blood lactate peaked at 6.4 minutes, with SWL and SWR showing biphasic patterns.
• A strong correlation was found between time-series SWL/SWR data and BL levels (p < 0.001).
• A multivariate regression model accurately predicted BL (R² = 0.763, p < 0.001).

Conclusions:

• A regression-based approach using sweat data is feasible for estimating BL dynamics.
• Non-invasive monitoring of SWL and SWR shows promise for assessing high-intensity exercise.
• Wearable sensor technology can provide valuable insights into physiological responses.