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Anaerobic threshold using sweat 
lactate sensor under hypoxia
Hiroki Okawara 1,4, Yuji Iwasawa 2,4, Tomonori Sawada 1, Kazuhisa Sugai 3, Kyohei Daigo 2, 
Yuta Seki 2, Genki Ichihara 2, Daisuke Nakashima 1, Motoaki Sano 2, Masaya Nakamura 1, 
Kazuki Sato 3, Keiichi Fukuda 2 & Yoshinori Katsumata 2,3*
We aimed to investigate the reliability and validity of sweat lactate threshold (sLT) measurement 
based on the real-time monitoring of the transition in sweat lactate levels (sLA) under hypoxic 
exercise. In this cross-sectional study, 20 healthy participants who underwent exercise tests using 
respiratory gas analysis under hypoxia (fraction of inspired oxygen  [FiO2], 15.4 ± 0.8%) in addition to 
normoxia  (FiO2, 20.9%) were included; we simultaneously monitored sLA transition using a wearable 
lactate sensor. The initial significant elevation in sLA over the baseline was defined as sLT. Under 
hypoxia, real-time dynamic changes in sLA were successfully visualized, including a rapid, continual 
rise until volitionary exhaustion and a progressive reduction in the recovery phase. High intra- and 
inter-evaluator reliability was demonstrated for sLT’s repeat determinations (0.782 [0.607–0.898] and 
0.933 [0.841–0.973]) as intraclass correlation coefficients [95% confidence interval]. sLT correlated 
with ventilatory threshold (VT) (r = 0.70, p < 0.01). A strong agreement was found in the Bland–Altman 
plot (mean difference/mean average time: − 15.5/550.8 s) under hypoxia. Our wearable device enabled 
continuous and real-time lactate assessment in sweat under hypoxic conditions in healthy participants 
with high reliability and validity, providing additional information to detect anaerobic thresholds in 
hypoxic conditions.
It is presumed that hypoxic training helps improve endurance performance in  athletes1. Traditional high-altitude 
training refers to a state where atmospheric and oxygen pressure decrease and athletes are exposed to chronic 
hypobaric hypoxia for many  weeks2. Recent studies on normobaric hypoxic exercise have investigated the impact 
of the recently popular live low-train high-altitude interventions on athletes’  lifestyles3,4, as prolonged exposure 
to low-pressure conditions is not always feasible (travel time, engagement, and expenses) and can lead to health 
 problems5,6. Anaerobic threshold (AT) and peak oxygen uptake (peak  VO2) should be routinely assessed in 
hypoxic conditions to practice efficient fitness training during  hypoxia7. To date, the ventilatory threshold (VT), 
calculated as a noninvasive index of metabolic response to incremental exercise, has been used to determine  AT8,9. 
The VT assessment method is beneficial; however, VT assessment requires an expensive analyzer and expertise 
due to the difficulty in confirming VT based on the oscillations in minute ventilation and inconsistencies among 
several  factors10. The difference in expertise is reported to worsen the VT determination  agreement11. This is 
because the respiratory gas analyzer is not readily available in sports settings. Therefore, there is an urgent need 
to apply an innovative and simple system to determine AT with high reliability for fitness training under hypoxia.
Flexible, wearable sensing devices can yield vital information about the underlying physiology of a human 
participant in a continuous, real-time, and noninvasive  manner12,13. Sampling human sweat, rich in physiologi-
cal information, can enable noninvasive  monitoring14. We developed a sweat sensor to monitor sweat lactate 
levels (sLA) in real-time during progressive exercise in the clinical setting, investigating its use in detecting AT 
in healthy individuals and patients with cardiovascular  diseases15. sLA has been reported to not reflect blood 
lactate during  exercise16,17; however, our research group has examined sLA transitions during incremental load 
exercise and reported that the sweat lactate threshold was strongly approximated to AT by focusing on the inflec-
tion point where the value increases rapidly during incremental exercise, not the absolute  value15,18.
Our sLA sensor is portable and easy to carry, enabling convenient measurements in various environments, 
and the continuous collection of only 1 Hz sLA values promises a simpler determination of the inflection point. 
Moreover, the need for invasive collection methods, including blood collection, is undesirable considering human 
OPEN
1Department of Orthopaedic Surgery, Keio University School of Medicine, Tokyo, Japan. 2Department of 
Cardiology, Keio University School of Medicine, Tokyo, Japan. 3Institute for Integrated Sports Medicine, Keio 
University School of Medicine, Tokyo, Japan. 4These authors contributed equally: Hiroki Okawara and Yuji 
Iwasawa. *email: goodcentury21@keio.jp
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resources for multi-measurements, the possibility of any person to evaluate, and acceptance of the evaluation 
target.
Under hypoxia, some researchers previously reported AT evaluation  results7. However, similar to normoxia, 
it is problematic that the VT evaluation method was applied to broadly cover the sports setting. Therefore, we 
aimed to investigate the validity of AT estimation and reliability of the sLA continuously obtained using our sLA 
sensor during exercise under hypoxia in healthy participants.
Results
The baseline characteristics of the healthy participants are summarized in Table 1. The participants were males 
(100%) with a median (IQR) age of 21 (20–21) years. The temperature and humidity were 28 ± 1 °C and 62 ± 6% 
under hypoxia, respectively.
Figures 1 and 2 show the sLA during exercise in hypoxia. During the exercise tests, dynamic changes in the 
sLA were continuously measured and projected onto the wearable device without delay, even under hypoxia. 
Because of the lack of sweat, the lactate biosensor measured a negligible current response at the commence-
ment of cycling activity. During exercise, sLA increased drastically, and the sweat rate continuously increased 
as cycling continued until volitional exhaustion. This drastic sLA increase was not associated with the onset of 
sweating (Fig. 1).
Contrary to sLA, the heart rate and  VO2 gradually increased from incremental-load exercise initiation to 
its end (Fig. 2). At the end of the exercise period, the sLA continued to decrease relatively slowly, mirroring the 
decrease in heart rate. The results under normoxia are shown in Supplementary Figs. 1 and 2.
We easily identified the conversion from steady low lactate values to a continuous increase under hypoxia. 
Repeated sLT and VT determinations by the same evaluator demonstrated high intra-evaluator reliability 
Table 1.  Baseline characteristics of participants. Data are presented as median (IQR). BMI body mass index.
Demographic and anthropometric data
Healthy male participants (n = 20)
Age (years)
21.0 (20.0–21.0)
Height (cm)
174.0 (171.0–176.4)
Body weight (kg)
69.0 (62.5–71.1)
BMI (kg/m2)
22.2 (20.5–23.4)
Body fat/body weight (%)
14.2 (12.3–16.7)
Body muscle/body weight (%)
81.3 (78.6–83.1)
Body water/body weight (%)
60.2 (57.5–62.8)
Figure 1.  Imaging of sweat lactate levels, local sweat rate, and blood lactate values during incremental exercise 
under hypoxia. Representative graphs of sweat lactate levels (orange), local sweat rate (blue), and blood lactate 
values (red) during hypoxic exercise with a stepwise incremental protocol (25 W/min) ergometer are shown. VT 
ventilatory threshold, sLT sweat lactate threshold.
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(intraclass correlation [ICC] [2, 1] measured value [95% confidence interval] normo, 0.893 [0.794–0.952]; hypo, 
0.782 [0.607–0.898]; and normo, 0.711 [0.500–0.861]; hypo, 0.919 [0.841–0.964]), respectively (Fig. 3 and Sup-
plementary Fig. 3). Moreover, these were reproducible between both blinded reviewers (ICC [1, 1] measured 
value [95% confidence interval]; sLT; normo, 0.898 [0.765–0.958], hypo, 0.933 [0.841–0.973], and VT, normo, 
0.933 [0.841–0.973], hypo, 0.836 [0.638–0.931]) as shown in Table 2 and Supplementary Table 1. However, the 
intra- and inter-evaluation reliability for bLT was low (ICC [1, 1] measured value [95% confidence interval]; 
normo, 0.529 [0.134–0.781], hypo, 0.652 [0.314–0.845], ICC [2, 1] measured value [95% confidence interval]; 
normo, 0.621 [0.363–0.813], hypo, 0.586 [0.331–0.790]) as shown in Fig. 3, Table 2, Supplementary Fig. 3, and 
Supplementary Table 1.
The relationships between sLT and VT are shown in Fig. 4A and Supplementary Fig. 4A, describing the 
strong relationships between each threshold (normo, r = 0.69; hypo, r = 0.70). The Bland–Altman plot revealed 
that the mean difference between each threshold was 4.9 s under normoxia and − 15.5 s under hypoxia, and 
there was no bias between the mean values, displaying strong agreements between sLT and VT (Fig. 4B and 
Supplementary Fig. 4B).
Discussion
The noninvasive sLA sensor enabled continuous and real-time measurement of sLA during an exercise test 
under hypoxia. Furthermore, sLT determination had high intra- and inter-evaluator reliability, and sLT was 
strongly correlated with VT. Real-time sweat lactate monitoring could be applied to detect aerobic threshold, 
even under hypoxia (Fig. 5).
Lactate levels are measured to track an individual’s performance and exertion  level19,20. Blood lactate levels 
are measured by athletes or their  supporters21,22, but these are not continuous, real-time measurements, limiting 
their utility to applications where stationary, infrequent tests are sufficient. In particular, applying bLT relies on 
Figure 2.  Measured parameters in hypoxia. The graph shows the measured parameters [(a)  VO2/body weight, 
(b) Heart rate, (c) Sweat lactate, (d) sweat rate] at rest, warm up, VT, and peak in hypoxia. Data are shown as 
mean (± standard deviation). VO2 oxygen uptake, VT ventilatory threshold, HR heart rate, sLA sweat lactate, SR 
sweat rate.
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the measurement’s reliability; in this study, the intra- and inter-evaluation reliability for bLT was low. Conversely, 
even under hypoxia, our devices captured the sLA during fitness in a real-time, noninvasive, and continuous 
manner at 1 Hz instead of cumulative values as in the conventional method, which detects the “timing of change” 
in a real-time and sensitive manner. Therefore, it is easy to identify the inflection point (sLT) from the plots of the 
sLA values. Using sLT demonstrated lower intra- and inter-observer bias and superior determination accuracy. 
Figure 3.  Reliability testing of the time at sLT determined by the same evaluator in hypoxia. (a) The graph 
shows the relationship between the repeatedly determined sweat lactate threshold (sLT) by the same evaluator. 
(b) The graph shows the Bland–Altman plots, which indicate the respective differences between the repeatedly 
determined sLT by the same evaluator (y-axis) for each individual against the mean of the time at the repeatedly 
determined sLT (x-axis) in hypoxia. R correlation coefficient, p p-value, VT ventilatory threshold, sLT sweat 
lactate threshold.
Table 2.  Intra-evaluator reliability of sweat lactate threshold determination in hypoxia. ICC intraclass 
correlation, sLT sweat lactate threshold, bLT blood lactate threshold, VT ventilatory threshold, SD standard 
deviation.
Hypoxia
N
Evaluator 1
Evaluator 2
Evaluator 3
ICC (95% CI)
sLT [s]
Mean
20
553.3
486.3
533.6
0.782 (0.607–0.898)
SD
84.4
89.8
80.8
bLT [s]
Mean
20
581.8
558.7
597.6
0.586 (0.331–0.790)
SD
69.7
58.4
73.7
VT [s]
Mean
20
546.8
547.2
540.9
0.919 (0.841–0.964)
SD
66.2
49.8
59.7
Figure 4.  Validity testing of the time at VT and sLT in hypoxia. (a) The graph shows the relationship between 
the time from the start of the measurement (seconds) at VT and sLT. (b) The graph shows the Bland–Altman 
plots, which indicate the respective differences between the time from the start of measurement (s) at the VT 
and sLT (y-axis) for each individual against the mean of the time at the VT and sLT (x-axis) in hypoxia. R 
correlation coefficient, VT ventilatory threshold, sLT sweat lactate threshold.
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Another possible explanation to support this positive result is that several operations, including the exchange 
of the sensor chip, cleaning the upper arm which the sensor fixed, and flushing out any residual sweat from the 
duct in the perspiration meter, certainly could eliminate the bias due to contaminations from previous experi-
ments or original sweating. sLA has been reported to not reflect blood lactate during  exercise16,17; however, our 
data showed that the AT point coincided with that in the sLA level during progressive exercise, consistent with 
the finding of the previous  report15,18. This could be because an increase in lactate production from muscle cells, 
reflecting LT, may induce a simultaneous rise in sLA levels through changes in autonomic nervous balance, hor-
mones, acid–base equilibrium, and metabolic  dynamics23,24 similar to  VT25. A previous study has demonstrated 
a rapid increase in blood catecholamine concentrations during incremental exercise  loads26. Furthermore, it has 
also been indicated that sweat gland metabolism is activated by  catecholamines27. Therefore, we are evaluating 
the timing of physiological responses to increasing exercise loads using completely different analytes and not 
estimating the bLA levels by observing the sweat lactate levels.
Measuring VT and peak  VO2 with respiratory gas analysis helps in efficient training under hypoxia. However, 
it is often difficult to determine VT because of inconsistencies among the several factors required for detecting 
VT, such as the ventilation (VE)/oxygen uptake  (VO2) or carbon dioxide production  (VCO2)/VO2 slope and oscil-
lations in minute  ventilation10. Further, a respiratory gas analyzer is unavailable in a small hypoxic booth because 
of its size. Moreover, using a facemask, respiratory gas cannot be collected under hypoxic exercise. In addition, 
in a respiratory infection epidemic such as COVID-19, using respiratory gas analyzers has become difficult due 
to the possibility of cross-infection. Determining sLT using only sweat-based monitoring could overcome these 
problems, and the newly developed device enables AT measurements in various hypoxic environments (a small 
private booth and facemask).
It has been reported that sweat rate decreases in  hypoxia28. As our sensor showed non-response in the absence 
of sweating, evaluating sweat rate is paramount to successfully determining sLT in hypoxia. This study quantified 
the amount of sweating per unit area near the sensor; the results showed no difference in the local sweat rate 
during exercise under hypoxia compared with that under normoxia. The relationship between the local sweat 
rate/response in the sLA sensor, humidity, and temperature during exercise warrants further investigation.
Figure 5.  Schematic of the lactate-sensing device under hypoxia. This figure is licensed by © Medical FIG. ICC 
interclass correlation coefficients, bLT blood lactate threshold, VCO2 carbon dioxide output, VO2 oxygen uptake, 
VT ventilatory threshold, sLT sweat lactate threshold, FiO2 fraction of inspiratory oxygen.
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The device used in our study is suitable for use in remote monitoring or remote training settings during 
isolation measures, such as those taken during a respiratory infection epidemic. Furthermore, real-time assess-
ments of sLA through a wireless data transfer system can offer a rigorous training menu under hypoxia based 
on the day-to-day physical conditions of trainees. In addition, exercise under hypoxia has been recognized as 
a new therapeutic modality for health promotion and disease prevention or treatment, such as for  diabetes29, 
cardiovascular  diseases30,  hypertension31,  obesity32, and age-related  diseases33. Disease prevention and treatment 
can be more efficiently and safely provided by combining sLA sensors with exercise under hypoxia.
The study has some limitations. First, due to the observational study design, we could not exclude the influ-
ence of a selection bias. Second, our study had a relatively small number of cases. Third, the current study 
included healthy college-aged male individuals. Recent findings could be applied to various age groups and 
genders; however, further research, including females and young athletes, is required considering a sweat func-
tional difference between sexes. Fourth, the sLA sensor used in this study exhibited the current value, not the 
sLA concentration. Conversion to concentration from the current value is possible; however, it is sufficient to 
display the current values to determine the inflection point based on the constant value of sLA during exercise. 
The effect of sLA dilution by high sweat rate on sLT determination is minimal due to the low sweat rate at AT 
and, therefore, does not negate our study’s result. Finally, exercise training has been performed under various 
hypoxic conditions; however, only a hypoxia of 15.5% was verified. Further verification is required to overcome 
these limitations.
In conclusion, the noninvasive sweat lactate sensor enabled continuous and real-time measurement of sweat 
lactate during exercise under hypoxia. The sweat lactate threshold can also be reliably determined by non-
experts, even under hypoxia. Real-time sweat lactate monitoring could be used to detect aerobic threshold in 
a noninvasive and feasible manner under hypoxia and normoxia. It is expected that these findings enhance the 
effectiveness of exercise under hypoxia. This was the first study to show real-time monitoring of sLA during 
progressive exercise under hypoxia. Given the difficulty in deciding VT, such as in hypoxia, sLA monitoring 
could be beneficial in improving VT detection with high reliability.
Methods
Experimental approach to the problem
We conducted a cross-sectional study with 20 healthy participants who underwent exercise tests with respiratory 
gas analysis under hypoxia or normoxia and simultaneously monitored changes in sLA using a wearable lactate 
sensor to investigate the capability of sweat lactate sensor to monitor sLA under hypoxia and the relationship 
between sLT and VT. In addition, Intraclass correlation was determined for the intra- and inter-evaluator reli-
ability of each threshold in this study.
Subjects
Participants aged 20–80 years were recruited through a web system in June 2021. The exclusion criteria were 
patients receiving medication, having comorbidities like hypertension, diabetes, and active lung diseases, and 
having low local sweat rates of < 0.4 mg/cm2/min at the upper arm during maximal exercise. This sweat rate 
threshold was defined based on previous  reports15 and preliminary studies. Twenty healthy participants were 
enrolled, including athletes and those with a broad spectrum of aerobic capacities and fitness levels. Notably, all 
participants exercised regularly for more than twice weekly.
The study protocol was approved by the Institutional Review Board (IRB) of Keio University School of Medi-
cine (approval number 20190229), and the study was conducted following the principles of the Declaration of 
Helsinki. Verbal informed consent was obtained from all participants because the IRB approved using verbal con-
sent following the Japanese guidelines for clinical research. Verbal consent was recorded as an experimental note.
Procedures
The twice exercise tests with a minimum of 2 days intervals were performed using an electromagnetically braked 
ergometer (POWER MAX V3 Pro, Konami Sports Co., Ltd., Tokyo, Japan) with respiratory gas analysis under 
hypoxia (hypo; a fraction of inspired oxygen  [FiO2], 15.4 ± 0.8% equivalent to a simulated altitude of 2500 m) or 
normoxia (normo;  FiO2, 20.9%). Hypoxic conditions were created in an exercise booth with an oxygen filtration 
hypoxic generator (Hypoxico Everest Summit II; WILL Co., Tokyo, Japan) by insufflating nitrogen as a target of 
 FiO2 15.5%34. During exercise, the sLA was monitored using an sLA sensor (Grace Imaging Inc., Tokyo, Japan) 
attached to the upper arm, and the local sweat rate was measured at a sampling rate of 1 Hz in the same area as 
the sLA sensor using a perspiration meter (SKN-2000M; SKINOS Co., Ltd., Nagano, Japan). A perspiration meter 
ensured the value returned to zero before the new experiment by flushing out any residual sweat from the duct. 
Heart rate was monitored using Duranta (Zaiken, Tokyo, Japan), and blood lactate levels were measured using 
a standard enzymatic method on a lactate analyzer (Lactate  Pro2®, ARKRAY, Kyoto, Japan).
On the day of the exercise test, the participants avoided any prior heavy physical activity. The participants 
performed the test upright on an electronically braked ergometer. Following a 2-min rest to stabilize the heart rate 
and respiratory condition, the participants performed a 4-min warm-up pedaling at 20 W. Then, they exercised at 
increasing intensity until they could no longer maintain the pedaling rate (volitional exhaustion). The resistance 
was increased in 25-W increments from 50-W at 1-min intervals. Once the exercise tests were terminated, the 
participants were instructed to stop pedaling and remain on the ergometer for 3 min.
The expired gas flow collected through the mask was measured using a breath-by-breath automated system 
 (Aeromonitor®, Minato Medical Science Co., Ltd., Osaka, Japan). This system was subjected to a three-way cali-
bration process involving a flow volume sensor, gas analyzer, and delay time calibration. The gas analyzer was 
calibrated under hypoxia using 8%  O2, assuming a minimum oxygen concentration of 8% in exhaled air during 
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hypoxic exercise. Respiratory gas exchange, including VE,  VO2, and  VCO2, was continuously monitored and 
measured using a 10-s average. VT was determined using the ventilatory equivalent, excess carbon dioxide, and 
modified V-slope  methods10 through manual operating software. First, two of the three experienced researchers 
independently and randomly evaluated each participant’s VT using the three methods. The researchers used 
all three methods to assess concurrent breakpoints and eliminate false breakpoints. Second, if the  VO2 values 
determined by the independent researchers were within 3%, then the  VO2 values from the two investigators were 
averaged. Third, if the  VO2 values determined by the independent evaluators were not within 3% of one another, 
a third researcher independently determined  VO2. The third  VO2 value was then compared with that obtained 
by the initial investigators. If the adjudicated  VO2 value was within 3% of either of the initial investigators, the 
two  VO2 values were averaged.
Blood lactate values were obtained by auricular pricking and gentle squeezing of the ear lobe to obtain a 
capillary blood sample at rest, warm-up, and every minute after the start of progressive intensity. The samples 
were immediately analyzed for whole-blood lactate concentrations (mmol/L).
bLT was determined through graphical plots of the bLA value vs.  time8. Visual interpretation was indepen-
dently made for each participant by two experienced researchers to locate the first rise from baseline. If the 
independent determinations of the stage at LT differed between the two researchers, a third researcher adjudi-
cated the difference by independently determining LT. The three researchers then jointly agreed on the LT point.
The sLA was measured using a sLA sensor, which quantifies lactate concentration as a current value because 
it reacts with sLA and generates an electric  current15. The sLA sensing system comprises a disposable sensor chip 
and a sensor. The sensor chip generates the current value proportional to the lactate concentration by catalyzing 
the enzymatic immobilization on its surface to oxidize lactate, which reduces hydrogen peroxide. In addition, 
a protective film formed by exposure using a UV lamp allows the achievement of immediate responsiveness 
(response delay < 1 s) and sustainability without the enzyme reacting all at  once15. The current value can be 
obtained as continuous data within 0.1–80 μA in 0.1-μA increments. The sLA sensor responded linearly to the 
lactate concentrations, especially in the 0–5 mmol/L range, which were most significant in determining the LT 
because the LT had normal lactate values from 2 to 4 mmol/L15.
Moreover, it is also validated that the sLA values obtained from this sensor can show a significant enough difference 
to determine the inflection point under various sweat  environments35. After calibration using saline for 2 or 3 min, the 
sensor chip connected to the sensor device was attached to the superior right upper limb of the participants and cleaned 
with an alcohol-free cloth to eliminate the influence of original sweat. In addition, the data were recorded at a sampling 
frequency of 1 Hz for mobile applications with Bluetooth connection. The recorded data were converted to moving 
average values over 13-s intervals and underwent zero correction using the baseline value. sLT was defined as the first 
significant increase in the sLA above baseline based on graphical  plots15. Three researchers, independent of those who 
analyzed respiratory gas exchange, agreed on the point of sLT.
Statistical analyses
The results are represented as mean ± standard deviation for continuous variables and percentages for categorical 
variables, as appropriate. ICC was determined for intra- and inter-evaluator reliability of each  threshold36. The 
intra-evaluator reliability was tested by one of the blinded reviewers. The inter-observer reliability was tested 
by estimating each threshold using three blinded reviewers. The relationship between exercise time at sLT and 
VT was investigated using Pearson’s correlation coefficient test. In addition, the Bland–Altman technique was 
applied to verify the similarities among the different  methods37. The graphical representation of the difference 
between the methods and the average WAS compared. Statistical significance was set at two-tailed p-values < 0.05. 
All statistical analyses were performed using IBM SPSS Statistics for Windows, version 27.0 (IBM Corporation, 
Armonk, NY, USA).
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author upon 
reasonable request.
Received: 5 July 2023; Accepted: 7 December 2023
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Acknowledgements
We are grateful to Editage for editing this manuscript. This study was funded by the Grant-in-Aid from Scien-
tific Research from the Japan Agency for Medical Research and Development (ID. 21ek0210130h0003) and by a 
grant from the Kimura Memorial Heart Foundation Research Grant for 2019, Suzuken Memorial Foundation, 
Foundation for Total Health Promotion, and Research Grant for Public Health Science. The funders had no role 
in study design, data collection and analysis, and the decision to publish or prepare the manuscript.
Author contributions
The author contributions are stated as follows; the manuscript was drawn by Y.I., H.O., and T.S. The images were 
prepared by Y.I., H.O., T.S., and Y.K. The patient information was collected by H.O., T.D., K.S., K.D., Y.S., G.I., 
D.N., and Y.K. A critical revision of the manuscript for key intellectual content and supervision was provided by 
M.S., K.S., K.F., and Y.K. All authors have approved all aspects of our work, read, and approved the manuscript.
Competing interests 
D.N. is a founder and shareholder of Grace Imaging Inc. All other authors declare no competing interests.
Additional information
Supplementary Information The online version contains supplementary material available at https:// doi. org/ 
10. 1038/ s41598- 023- 49369-7.
Correspondence and requests for materials should be addressed to Y.K.
Reprints and permissions information is available at www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and 
institutional affiliations.
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Anaerobic threshold using sweat lactate sensor under hypoxia.
Okawara, Hiroki,Iwasawa, Yuji,Sawada, Tomonori,Sugai, Kazuhisa,Daigo, Kyohei,Seki, Yuta,Ichihara, Genki,Nakashima, Daisuke,Sano, Motoaki,Nakamura, Masaya,Sato, Kazuki,Fukuda, Keiichi,Katsumata, Yoshinori