Open Access Journal of Sports Medicine (Jul 2025)

Short Report: Estimating Blood Lactate Dynamics from Sweat Lactate and Sweat Rate After High-Intensity Exercise – A Pilot Regression-Based Study

  • Hattori M,
  • Yashiro K

Journal volume & issue
Vol. Volume 16, no. Issue 1
pp. 99 – 105

Abstract

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Masaaki Hattori,1 Kazuya Yashiro2 1Department of Community Development, Tokai University, Sapporo, Hokkaido, Japan; 2Faculty of Information Technology, Kanagawa Institute of Technology, Atsugi, Kanagawa, JapanCorrespondence: Masaaki Hattori, Department of Community Development, Tokai University, 5-1-1, Minamisawa, Minami-ku, Sapporo, 005-8601, Japan, Tel +81 11 571 1111 (ext 2423), Fax +81 11 571 7879, Email [email protected]: Blood lactate (BL) is a critical biomarker for assessing anaerobic metabolism and fatigue. Sweat lactate (SWL) and sweat rate (SWR) have been explored as non-invasive alternatives, but their capacity to estimate BL dynamics after short-term high-intensity exercise remains unclear.Purpose: This pilot study aimed to evaluate whether BL dynamics can be predicted using a regression model based on the time-series patterns of SWL and SWR measured by wearable sensors.Methods: Five healthy male athletes (three sprinters and two endurance runners) performed a 30-second Wingate anaerobic test. SWL and SWR were continuously monitored using a wearable electrochemical sensor and a ventilated capsule-type sweat rate meter. Capillary BL was sampled for 30 minutes post-exercise.Results: BL showed a delayed peak at 6.4 ± 1.2 min, while SWL and SWR exhibited biphasic responses. The second SWL peak (7.5 ± 2.2 min) aligned with the BL peak. Although peak-based correlations were not significant, Pearson correlations using time-series data revealed strong associations (r = 0.501– 0.933 for SWL; r = 0.515– 0.805 for SWR; all p < 0.001). A multivariate regression model using both variables predicted BL with high accuracy (R² = 0.763, RMSE = 1.612, MAE = 0.995, p < 0.001).Conclusion: These findings support the feasibility of a regression-based approach using sweat-derived time-series data to non-invasively estimate BL dynamics after high-intensity exercise.Keywords: sweat lactate, sweat rate, blood lactate, wearable sensor, high-intensity exercise, non-invasive

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