Hattori Masaaki, Yashiro Kazuya
Department of Community Development, Tokai University, Sapporo, Hokkaido, Japan.
Faculty of Information Technology, Kanagawa Institute of Technology, Atsugi, Kanagawa, Japan.
Open Access J Sports Med. 2025 Jul 30;16:99-105. doi: 10.2147/OAJSM.S534243. eCollection 2025.
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.
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.
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.
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 ( = 0.763, RMSE = 1.612, MAE = 0.995, p < 0.001).
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.
血乳酸（BL）是评估无氧代谢和疲劳的关键生物标志物。汗液乳酸（SWL）和出汗率（SWR）已被探索作为非侵入性替代指标，但其在短期高强度运动后估计血乳酸动态变化的能力仍不明确。
本初步研究旨在评估是否可以使用基于可穿戴传感器测量的SWL和SWR时间序列模式的回归模型来预测血乳酸动态变化。
五名健康男性运动员（三名短跑运动员和两名耐力跑运动员）进行了30秒的温盖特无氧测试。使用可穿戴电化学传感器和通风胶囊式出汗率计连续监测SWL和SWR。运动后30分钟采集毛细血管血乳酸样本。
血乳酸在6.4±1.2分钟出现延迟峰值，而SWL和SWR呈现双相反应。SWL的第二个峰值（7.5±2.2分钟）与血乳酸峰值一致。尽管基于峰值的相关性不显著，但使用时间序列数据的Pearson相关性显示出强关联（SWL的r = 0.501 - 0.933；SWR的r = 0.515 - 0.805；所有p < 0.001）。使用这两个变量的多元回归模型能够高精度地预测血乳酸（ = 0.763，RMSE = 1.612，MAE = 0.995，p < 0.001）。
这些发现支持了一种基于回归的方法的可行性，即使用汗液衍生的时间序列数据来无创估计高强度运动后的血乳酸动态变化。
