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Estimation of maximal lactate steady state using the sweat lactate sensor.

Yuki MuramotoDaisuke NakashimaTsubasa AmanoTomota HaritaKazuhisa SugaiKyohei DaigoYuji IwasawaGenki IchiharaHiroki OkawaraTomonori SawadaAkira KinodaYuichi YamadaTakeshi KimuraKazuki SatoYoshinori Katsumata
Published in: Scientific reports (2023)
A simple, non-invasive algorithm for maximal lactate steady state (MLSS) assessment has not been developed. We examined whether MLSS can be estimated from the sweat lactate threshold (sLT) using a novel sweat lactate sensor for healthy adults, with consideration of their exercise habits. Fifteen adults representing diverse fitness levels were recruited. Participants with/without exercise habits were defined as trained/untrained, respectively. Constant-load testing for 30 min at 110%, 115%, 120%, and 125% of sLT intensity was performed to determine MLSS. The tissue oxygenation index (TOI) of the thigh was also monitored. MLSS was not fully estimated from sLT, with 110%, 115%, 120%, and 125% of sLT in one, four, three, and seven participants, respectively. The MLSS based on sLT was higher in the trained group as compared to the untrained group. A total of 80% of trained participants had an MLSS of 120% or higher, while 75% of untrained participants had an MLSS of 115% or lower based on sLT. Furthermore, compared to untrained participants, trained participants continued constant-load exercise even if their TOI decreased below the resting baseline (P < 0.01). MLSS was successfully estimated using sLT, with 120% or more in trained participants and 115% or less in untrained participants. This suggests that trained individuals can continue exercising despite decreases in oxygen saturation in lower extremity skeletal muscles.
Keyphrases
  • resistance training
  • high intensity
  • body composition
  • machine learning
  • physical activity
  • heart rate
  • deep learning
  • blood pressure