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A novel device for detecting 
anaerobic threshold using sweat 
lactate during exercise
Yuta Seki1,2,6, Daisuke Nakashima3,6*, Yasuyuki Shiraishi1,2, Toshinobu Ryuzaki1,2, 
Hidehiko Ikura1,2, Kotaro Miura1,2, Masato Suzuki1, Takatomo Watanabe4, 
Takeo Nagura3,5, Morio Matsumato3, Masaya Nakamura3, Kazuki Sato2, Keiichi Fukuda1 & 
Yoshinori Katsumata1,2*
The lactate threshold (LT1), which is defined as the first rise in lactate concentration during 
incremental exercise, has not been non-invasively and conveniently determined in a clinical setting. 
We aimed to visualize changes in lactate concentration in sweat during exercise using our wearable 
lactate sensor and investigate the relationship between the lactate threshold (LT1) and ventilatory 
threshold (VT1). Twenty-three healthy subjects and 42 patients with cardiovascular diseases (CVDs) 
were enrolled. During exercise, the dynamic changes in lactate values in sweat were visualized in real-
time with a sharp continuous increase up to volitional exhaustion and a gradual decrease during the 
recovery period. The LT1 in sweat was well correlated with the LT1 in blood and the VT1 (r = 0.92 and 
0.71, respectively). In addition, the Bland–Altman plot described no bias between the mean values 
(mean differences: − 4.5 and 2.5 W, respectively). Continuous monitoring of lactate concentrations 
during exercise can provide additional information for detecting the VT1.
Abbreviations
sLT 
 Lactate threshold in sweat
bLT 
 The lactate threshold in blood
VT1 
 Ventilatory threshold
CVD 
 Cardiovascular disease
WR 
 Work rate
NYHA 
 New York Heart Association Functional Classification
VE 
 Ventilation
VO2 
 Oxygen uptake
VCO2 
 Carbon dioxide production
Adequate regular physical activity is paramount to maintaining good  health1,2 and preventing cardiovascular 
diseases (CVD). Current clinical practice guidelines and expert statements recommend aerobic exercise for 
patients with  CVD3–5. Although an exercise test with respiratory gas analysis is the only non-invasive way to 
determine the ventilatory threshold (VT1)6 in clinical practice, VT1 assessment requires an expensive analyzer 
and  expertise5,7. Additionally, it is incidentally difficult to confirm the VT1 because of oscillations in minute 
ventilation and inconsistencies among several factors such as the VE/VO2, the terminal exhaled  O2 concentra-
tion, and the  VCO2/VO2  slope8. Further, careful attention is necessitated when using a respiratory gas analyzer 
due to possible cross-infection. An alternative method is needed to detect VT1 easily and precisely without the 
need for a respiratory gas analyzer.
Flexible wearable sensing devices can yield important information about the underlying physiology of 
a human subject in a continuous, real-time, and non-invasive  manner9,10. Sampling human sweat, which is 
rich in physiological information such as the sweat rate or sodium concentration, could enable non-invasive 
OPEN
1Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo 160-8582, 
Japan. 2Institute for Integrated Sports Medicine, Keio University School of Medicine, Tokyo, Japan. 3Department 
of Orthopaedic Surgery, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo 160-8582, 
Japan. 4Department of Clinical Laboratory, Gifu University Hospital, Gifu, Japan. 5Department of Clinical 
Biomechanics, Keio University School of Medicine, Tokyo, Japan. 6These authors contributed equally: Yuta Seki and 
Daisuke Nakashima. *email: nakashima@keio.jp; goodcentury21@keio.jp
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 monitoring11. To date, sweat-based and non-invasive biosensors of lactate have been reported in research 
 settings12–14 and have shown that sweat lactate is elevated in conjunction with exercise intensity. Therefore, the 
application of continuous lactate monitoring systems using wearable lactate sensors could influence exercise 
therapy in patients with CVD in clinical practice. However, these devices have not yet been applied in clinical 
practice, which might be due to unsuccessful miniaturization of devices and an operation that is easy to use, the 
inappropriate degree of accuracy as a medical device, or high cost. We have developed an innovative device in 
which sweat lactate may be monitored in a continuous, convenient, and non-invasive manner. We hypothesized 
that this device could be applied in clinical practice and successfully detected the lactate threshold (LT1) which 
is the first rise of lactate concentration during incremental exercise.
We aimed to investigate whether a usable device in the clinical setting would enable the continuous moni-
toring of sweat lactate during incremental exercise. Moreover, we elucidated the relationship among the lactate 
threshold in sweat (sLT), blood (bLT), and VT1 in healthy subjects and patients with CVD.
Results
In-vitro characterization of the lactate biosensor. 
Figure 1 shows the amperometric response of the 
lactate biosensor to increasing lactate concentrations in the physiological range of 0–10 mmol/L. The biosensor 
responded linearly to the lactate concentrations, especially in the ranges from 0 to 5 mmol/L, with a sensitivity of 
2.4 A/mM (Fig. 1A). The range of 0–5 mmol/L was important in determining the LT1 because lactate concentra-
tions around 2 mmol/L are related to LT1 /  VT16. Further, the sensors responded quickly and with almost the 
same value to L-lactate acid three times repeatedly (Fig. 1B).
Study subjects. 
The baseline characteristics of the healthy subjects are summarized in Table 1. The healthy 
subjects were predominantly male (91%), with a median age of 20 (IQR 20–21) years. Tables 1 and 2 dem-
onstrates the patient background of patients with CVD. The patients were predominantly male (76%), with a 
median age of 63 years (interquartile [IQR], 54–71) and left ventricular ejection fraction (LVEF) of 50% (IQR, 
31.7–58.5). Thirty-four (83%) patients were taking beta-blockers.
Monitoring of the lactate in sweat during exercise. 
Figure 2 and Online Supplemental Video S1 
show the lactate values in sweat during incremental exercise. Dynamic changes in sweat lactate values during 
the exercise tests were continuously measured and projected on the wearable device without delay in both the 
healthy subjects and a subset of patients with CVD. At the commencement of the cycling activity, negligible cur-
rent response was measured by the lactate biosensor due to the lack of sweat. At the onset of sweating, lactate 
was released from the epidermis, and was selectively detected by the LOx-based biosensor. During the exercise, a 
drastic increase in sweat lactate values was observed as the cycling continued up to volitional exhaustion (Fig. 2). 
At the end of the exercise period, sweat lactate values continued to decrease relatively slowly, compared to the 
decrease in heart rate.
Predictors associated with non-response in the lactate sensor. 
In patients with CVD, changes in 
sweat lactate values during exercise were similar to those in healthy subjects. However, 19 cases had steady low 
lactate values after starting the exercise until the recovery state (Online Fig. S1), suggesting a lack of sweat even 
with a maximum exercise load. Logistic regression analysis was performed to identify factors associated with 
non-response in the lactate sensor. The results of the univariate analyses are shown in Table 3. New York Heart 
Association functional classification (NYHA) 3 and low peak  VO2 were associated with non-response in the 
lactate sensor among patients with CVD (odds ratio [OR], 0.06; 95% confidence interval [CI] 0.01–0.24; and OR, 
Figure 1.  In-vitro characteristics of the sweat lactate sensor chip. (A) Amperometric response to increasing 
lactate concentration from 0 to 20 mM (0, 2.5, 5, 10, and 20 mM) in phosphate buffer (pH 7.0); the graph shows 
the corresponding calibration plots of the sensor. Applied voltage = 0.16 V versus Ag/AgCl. The data were 
obtained from three samples. (B) Reproducibility and long-term stability of the sweat lactate sensor; the graph 
shows amperometric response to l-lactic acid solution adjusted to 10 mM repeatedly three times for 90 s. Data 
recording was paused for 90 s for each response. The data were obtained from four samples.
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1.18; 95% CI 1.02–1.42, respectively). Conversely, age, B-type natriuretic peptide, and LVEF were not associated 
with a non-response in the lactate sensor (OR 1.03 [95% CI 0.66–1.61], OR 0.95 [95% CI 0.84–1.05], and OR 
1.17 [95% CI 0.95–1.48], respectively).
Relationship among the sLT and bLT. 
The conversion from the steady low lactate values to the continu-
ous increase easily detected in all healthy subjects and 23 patients with response in the lactate sensor (Fig. 2), was 
defined as sLT. Among the 23 CVD patients, the monitoring of blood lactate concentration during exercise was 
Table 1.  Baseline characteristics of healthy subjects and patients. ACEI angiotensin-converting enzyme 
inhibitor, ARB angiotensin receptor blocker, BMI body mass index, BNP B-type natriuretic peptide, IQR 
interquartile range, LVEF left ventricular ejection fraction, NYHA New York Heart Association Functional 
Classification, VE/VCO2 ventilation-carbon dioxide production, VO2 oxygen uptake, VT1 ventilatory 
threshold.
Demographic and anthropometric data
Healthy subjects (n = 23)
Patients (n = 42)
Age, years (median, IQR)
20 (20, 21)
63 (54, 71)
Male, n (%)
21 (91)
32 (76.2)
Height, cm (median, IQR)
171 (165, 175)
165 (159, 172)
Body weight, kg (median, IQR)
62 (58, 68)
62 (57, 71)
BMI, kg/m2 (median, IQR)
22 (20, 23)
23 (21, 25)
Hypertension, n (%)
–
12 (28.6)
Diabetes, n (%)
–
9 (21.4)
Dyslipidemia, n (%)
–
22 (52.4)
NYHA ≧3
–
19 (45.2)
Device, n (%)
–
4 (9.5)
Laboratory data
Hemoglobin, g/dL (median, IQR)
–
13.7 (12.5, 14.6)
Creatinine, mg/dL (median, IQR)
–
0.9 (0.8, 1.1)
BNP, pg/mL (median, IQR)
–
146.8 (35.6, 328.0)
Echocardiography data
LVEF, % (median, IQR)
–
49.5 (31.7, 58.5)
Medications
Beta-blocker, n (%)
–
34 (82.9)
ACEI or ARB, n (%)
–
24 (58.5)
Statin, n (%)
–
19 (45.2)
Antiplatelet drug, n (%)
–
16 (38.1)
Anti-arrhythmic drug, n (%)
–
3 (7.1)
Cardiopulmonary test data
VO2 at VT1, ml/kg/min (median, IQR)
–
10.5 (9.7, 12.3)
VT1, sec (median, IQR)
–
429.0 (391.5, 473.2)
Peak  VO2, mL/kg/min (median, IQR)
–
15.9 (12.4, 18.9)
%Peak  VO2, % (median, IQR)
–
67.5 (53.7, 79.7)
VE/VCO2 slope (median, IQR)
–
32.2 (28.6, 38.1)
Table 2.  Respiratory gas data during exercise in the patients. All values are presented as medians and IQRs. 
DBP diastolic blood pressure, HR heart rate, IQR interquartile range, RQ respiratory quotient, SBP systolic 
blood pressure, VE/VCO2 ventilation-carbon dioxide production, VO2 oxygen uptake, VT1 ventilatory 
threshold, WR work rate.
Rest
Warm-up
VT1
Peak
HR, bpm
70 (62, 82)
79 (70, 92)
96 (85, 109)
127 (113, 137)
SBP, mmHg
107 (91, 123)
118 (99, 130)
127 (110, 140)
142 (116, 166)
DBP, mmHg
69 (62, 80)
76 (67, 86)
75 (65, 82)
79 (72, 90)
VO2, mL/kg/min
3.6 (3.4, 4.1)
6.5 (5.6, 7.1)
11.5 (9.7, 12.3)
15.9 (12.4, 18.9)
RQ
–
–
0.89 (0.86, 0.97)
1.15 (1.08, 1.20)
WR (W)
–
0
46 (37, 58)
77 (62, 107)
VE/VCO2 slope
32.3 (28.6, 38.1)
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only enabled by 13 patients. Combining these 13 patients and all healthy subjects, the relationships between the 
WR-sLT and WR-bLT were investigated (Fig. 3A), which described a strong relationship between each thresh-
old (r = 0.92, P < 0.001). The Bland–Altman plot revealed that the mean difference between each threshold was 
− 4.5 W, and that there was no bias between the mean values, which displayed strong agreements between the 
WR-sLT and WR-bLT (Fig. 3B). Least-product regression analysis indicated no evidence of a fixed bias and a 
proportional bias (95% CI for y-intercept, − 9.16 to 19.1; 95% CI for the slope 0.854–1.020).
Relationship among the sLT and VT1. 
Similarly, a good correlation was observed between the WR-sLT 
and WR-VT1 (r = 0.71, P < 0.001; Fig. 4A). The Bland–Altman plot described a strong agreement in the patients 
with CVD (Fig. 4B; the mean difference between each threshold, 2.5 W). Least-product regression analysis indi-
cated a fixed bias (y-intercept, 22.7) and a proportional bias (slope, 0.57) between each threshold.
Figure 2.  Imaging of the lactate in the sweat during incremental exercise. Representative graphs (dots) of the 
lactate in sweat (LA in sweat; dark blue) and lactate in blood (LA in blood; red) during exercise with a RAMP 
(15 W/min) protocol ergometer are shown in the lower panel. The respiratory gas data was shown in the upper 
panel. HR heart rate, LA lactate, VE ventilatory equivalent, VE/VCO2 ventilation-carbon dioxide production, 
VE/VO2 ventilation-oxygen uptake, WR work rate.
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Discussion
The most striking result to emerge from our findings is that the non-invasive lactate sensor enabled continuous 
and real-time measurement of sweat lactate values during an incremental exercise test. Furthermore, sLT strongly 
correlated with both bLT and VT1 in a subset of patients with CVD as well as in healthy subjects. The real-time 
lactate monitoring in sweat could be applicable for the detection of the LT1.
Lactate has three roles including, acting as a major energy source, a gluconeogenic substrate, a cell signaling 
molecule, and is used as optimal fuel for working  muscles15. Muscle lactate production is essential to increase 
exercise  performance16, and lactate should be measured during exercise to track an individual’s performance 
and exertion  level17,18. Lactate values can be conventionally acquired via clinical labs or point-of-care  devices19,20; 
unfortunately, such approaches do not support continuous, real-time measurements, a fact that limits their utility 
to applications where stationary, infrequent tests are sufficient. Conversely, our devices captured the sweat lactate 
value during exercise in a real-time, continuous, and non-invasive manner in a subset of patients with CVD in 
addition to healthy subjects. Sweat lactate has been affected by the production of lactate in the body and the rate 
of sweating and metabolic dynamics in sweat  glands17,21. In addition, lactate is secreted into sweat, mirroring the 
intensity of exercise, but its concentration decreases with increasing sweat  volume17. Therefore, the sweat lactate 
Table 3.  Predictors of factors associated with response in the lactate sensor. ACEI angiotensin-converting 
enzyme inhibitor, ARB angiotensin receptor blocker, BMI body mass index, BNP B-type natriuretic peptide, 
CI confidence interval, LVEF left ventricular ejection fraction, NYHA New York Heart Association Functional 
Classification, VE/VCO2 ventilation-carbon dioxide production, VO2 oxygen uptake, VT1 ventilatory 
threshold.
Factor
Odds ratio (95% CI)
P-value
Age, year (per 10-point increase)
1.03 (0.66–1.61)
0.883
Male
2.19 (0.52–10.08)
0.288
Height, cm (per 10-point increase)
1.68 (0.82–3.75)
0.173
Body weight, kg (per 10-point increase)
1.30 (0.78–2.34)
0.339
BMI, kg/m2
1.03 (0.88–1.21)
0.748
Hypertension
0.48 (0.12–1.84)
0.285
Diabetes
0.59 (0.13–2.62)
0.485
NYHA 3 (versus ≤ 2)
0.06 (0.01–0.24)
 < 0.001
Hemoglobin, g/dL
1.18 (0.80–1.78)
0.414
Creatinine, mg/dL
0.93 (0.01–1.02)
0.736
BNP, pg/mL (per 50-point increase)
0.95 (0.84–1.05)
0.316
LVEF, % (per 5-point increase)
1.17 (0.95–1.48)
0.153
Beta-blocker
4.04 (0.75–31.20)
0.124
ACEI or ARB
1.24 (0.35–4.41)
0.732
VO2 at VT1, mL/kg/min
1.25 (0.95–1.71)
0.136
Peak  VO2, mL/kg/min
1.18 (1.02–1.42)
0.044
VE/VCO2 slope (per 5-point increase)
0.88 (0.57–1.31)
0.519
Figure 3.  Validity testing of the WR at the sLT and bLT. (A) The graph shows the relationship between the 
work rate (WR) at the sLT and bLT. (B) The graph shows the Bland–Altman plots, which indicate the respective 
differences between WR at the sLT, and bLT (y-axis) for each individual against the mean of the WR at the 
sLT, and bLT (x-axis). Triangles indicate the data of patients and circles indicate the data of healthy subjects. r 
correlation coefficient, 95% CI for b’ 95% confidence interval for the slope, 95% CI for a’ 95% confidence interval 
for y-intercept, SD standard deviation.
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concentration has been reported not to have reflected the blood lactate concentration in specific circumstances, 
such as during vigorous  exercise22. By combining the sweat lactate concentration with the sweat rate, the amount 
of lactate excreted from sweat may be calculated. Sweat lactate discharge may be more predictive of blood lactate 
levels than lactate concentrations. Further research is also warranted to examine this relationship.
Our sweat lactate sensor enabled the collection of sweat immediately after discharge unlike the other devices 
where sweat data represented a mixed state including previously discharged sweat. Therefore, the sensor used in 
this study successfully captured a rise in sweat lactate without delay. Moreover, despite the discrepancy between 
the sweat and blood lactate concentrations during vigorous exercise, there was no fixed and proportional biases 
between the WT-sLT and WT-sLT, which indicated that a rise in blood lactate coincided with that in sweat lactate 
during an incremental exercise. An increase in lactate production from muscle cells, reflecting the LT1, may 
induce a simultaneous rise in sweat lactate through a change in autonomic nervous balance, hormones, acid–base 
equilibrium, and metabolic  dynamics23–26. However, the mean difference between WR-sLT and WR-VT1 was 
small, but the SD was rather large, and the presence of fixed and proportional bias was also indicated, which sug-
gested a poor relationship between each threshold. This may have been caused by the difficulties in confirming 
the VT1 in some cases due to the inconsistencies among an increase in the ventilatory equivalent, excess  CO2, 
and modified V-slope methods.
Flexibility is crucial for unobtrusive wearable devices that cause no hindrance or irritation to the wearer. 
Recent advances in fabrication techniques have enabled the design of wearable sensing devices in thin, confor-
mal form that naturally comply with the smooth curvilinear geometry of human skin, thereby enabling close 
contact that is necessary for robust physiological measurements and monitoring of chemicals and electrolytes in 
 sweat27–29. Our sensor was highly flexible and can be smoothly adjusted to curved surfaces using PET substrates. 
The upper arm and forehead have a high-sweat rate during physical  excursion30–32 and can thus, serve as an 
appropriate area to measure lactate values in human sweat. Additionally, the epidermis and muscle tissues around 
the upper arm or forehead do not experience complex 3D strains and remain stable even during intense physical 
activities. Therefore, the sensor was attached to the upper arm in the healthy subjects considering easy operability 
for the use in outdoor sports or exercise. Conversely, in patients with CVD, who experience less sweating than 
healthy subjects, the sensor was attached to the forehead which has a higher-sweat  rate32. However, it was not 
possible to continuously measure lactate in patients with NYHA3 or low peak oxygen uptake. Non-response 
in the sensor indicates a lack of sweat during exercise, which could be caused by intravascular dehydration by 
diuretics, abnormality of autonomic nervous balance, such as dominant sympathetic activity, or frailty due to 
heart failure. Further research is also warranted to develop wearable devices to monitor lactate values in patients 
without efficient sweat production.
In clinical practice, exercise testing with respiratory gas analysis is the most useful way to determine VT1. 
However, it is often difficult to determine VT1 because of oscillations in minute ventilation and inconsisten-
cies among several factors such as the VE/VO2, the terminal exhaled  O2 concentration, and  VCO2/VO2  slope8. 
Furthermore, the use of a respiratory gas analyzer has a cross-infection possibility because of the closed circuit. 
The determination of sLT using only sweat-based monitoring could overcome these problems, and the device 
developed and used here would be suitable for use in a remote patient monitoring or remote rehabilitation set-
ting during isolation measures, such as that taken during the COVID pandemic. Further, real-time assessments 
of sweat lactate values through a wireless data transfer system can offer a rigorous aerobic exercise based on the 
day-to-day physical conditions of patients with CVD as well as healthy subjects (Online Fig. S2). This innova-
tive system could improve persistency of cardiac rehabilitation in outpatients and relocate their therapy from 
hospitals to other institutions, such as commercial fitness clubs or even patients’ homes.
Our findings should be interpreted with the following limitations. First, because of the observational study 
design, we could not deny the influence of selection bias and unmeasured confounders regarding the effect on 
Figure 4.  Validity testing of the WR at the sLT and bLT or VT1. (A) The graph shows the relationship between 
the work rate (WR) at the lactate threshold in sweat (WR-sLT) and WR-ventricular threshold (WR-VT1). 
(B) The graph shows the Bland–Altman plots, which indicate the respective differences between WR-sLT, 
and WR-VT1 (y-axis) for each individual against the mean of WR-sLT, and WR-VT1 (x-axis). r correlation 
coefficient, 95% CI for b’ 95% confidence interval for the slope, 95% CI for a’ 95% confidence interval for 
y-intercept, SD standard deviation.
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response in the lactate sensors. Second, our study had a relatively small number of cases and included no control 
group in which the sensors without lactate oxidase were used for comparison. To validate that lactate and not 
other sweat constituents was measured, a control experiment in which an unmodified (LOx-free) amperometric 
biosensor should be undertaken under the same experimental conditions. Future randomized-controlled studies 
with different medical centers are required to overcome these limitations. Third, sweat rate was not measured 
during exercise because of a lack of a sweat rate sensor in our device. Therefore, it is unknown whether non-
response in the lactate sensor is caused by a lack of sweat or rough contact necessary for robust physiological 
measurements. In addition, the exercise duration may be related to the amount of sweat. Exercise protocol 
improvements, such as a longer warm-up time, may overcome the lack of sweat in some cases. Further studies 
are needed to examine the relationship between nonresponse in the lactate sensor and a lack of sweat. Fourth, 
the sweat lactate sensor used in this study did not operate in an environment with a lack of sweat. It was thus 
not possible to measure changes in sweat lactate in the low-intensity range where there was no-sweating and in 
patients who did not sweat during exercise, such as with NYHA3 or low peak  O2 uptake.
Conclusions
This was the first study to show real-time monitoring of sweat lactate values during incremental exercise in 
patients with CVD as well as in healthy subjects. Given the difficult situation of deciding VT1, the monitoring 
of lactate values in sweat could be helpful for improving the detection of VT1.
Methods
Lactate measurement device. 
L-lactic acid and hydroxymethylferrocene were obtained from Tokyo 
Chemical Industry Co., Ltd. (Tokyo, Japan). Phosphate buffer solution (PBS) (0.1 mol/L, pH 7.0) was purchased 
from the Nacalai Tesque, Inc. (Kyoto, Japan). L-LOx (LCO-301) was purchased from the Toyobo Corp. (Osaka, 
Japan). Water for molecular biology (H20MB0501) was obtained from Merck KGaA (Darmstadt, Germany). 
Water-soluble photocurable photosensitive resin (BIOSURFINE-AWP) was obtained from Toyo Gosei Co., Ltd. 
(Tokyo, Japan). Methanol was obtained from FUJIFILM Wako Pure Chemical Corporation (Osaka, Japan). The 
original printing electrode chip (DEP-CHIP) was procured from Bio-Device Technology, Inc. (Ishikawa, Japan).
Instrumentation. 
The original printed electrode chip (hereinafter referred to as "printed electrode"), which 
is the base of the lactate sensor chip, was designed using computer-aided design with a pattern shape consisting 
of three poles: an acting electrode, a counter electrode, and a reference electrode (Fig. 5A). Subsequently, PET 
substrates (Toray Industries, Inc., Tokyo, Japan) were fabricated using carbon ink, Ag/AgCl and insulating ink 
in a screen-printing process. These processes were outsourced to Bio-Device Technology, Inc (Ishikawa, Japan).
Fabrication of lactate sensor chips. 
A total of 0.5 μL of hydroxymethylferrocene saturated methanol 
solution and 1.0 μL of L-LOX 0.5 wt% solution were applied to the working electrode of the printing electrode 
using a micropipette, and the working electrode was dried at room temperature (20–24 °C). The entire surface 
of the working electrode, counter electrode, and reference electrode was then coated with BIOSURFINE-AWP 
diluted to 3 wt% using pre-molecular biological water with an applicator to achieve a film thickness of 15 μm. 
Finally, the lactate sensor chip was fabricated by forming a protective film by exposure using a UV lamp (365 nm 
wavelength). The fabricated lactate sensor chips were kept refrigerated at 5 °C (Fig. 5A). When the biosensor 
contacts lactate, the immobilized Lox enzyme catalyzes the oxidation of lactate to generate pyruvate and  H2O2. 
The Prussian blue transducer then selectively reduces the  H2O2 to generate electrons to quantify the lactate 
concentration (Fig. 5B).
Lactate sensor device. 
Lactate concentration was determined by the voltage of the working electrode 
(WE) on the sensor chip via a potentiostat unit, driven through the I2C interface. During measurement, elapsed 
time from start (in seconds), A/D converted voltage (equivalent to lactate concentration), and temperature (in 
Celsius) were stored as 10-byte binary strip data on flash memory using SPI. An in-house mobile application 
then received the data from a connected device at 1 s intervals for 10–20 min. The operating voltage was regu-
lated to 3 V via an LDO regulator. The battery was charged via a USB Type-C cable and of the in-house mobile 
application was regularly notified its level. Power on/off and Bluetooth LE communication status had been indi-
cated for the user with LEDs (Fig. 5C).
In-vitro studies. 
The lactate concentration in human sweat depends on metabolism and level of exertion, 
and typically ranges from 0 to 20 mmol/L. A wide linear-detection range coupled with a fast response time 
is thus essential for continuous epidermal monitoring of lactate. Therefore, the electrochemical characteriza-
tion of the LA sensor chip was performed using l-lactic acid solutions in 0 (pH 7.0), 2.5 (pH 7.0), 5 (pH 6.9), 
10 (pH 6.8), and 20 (pH 6.6) mmol/L prepared in 0.1 mol/L phosphate buffer solution (PBS). Then, the three 
lactate sensor tips were evaluated using chronoamperometry at an overprinting voltage of 0.16 V (versus Ag/
AgCl). In addition, the four sensor tips were evaluated for a total of three times with L-lactate solution adjusted 
to 10 mmol/L, to evaluate the long-term stability of the sensor. The electrochemical characterization was per-
formed at room temperature (20–24 ℃using an electrochemical analyzer from Grace Imaging. Inc.
Lactate measurement in humans. 
Study sample and ethical approval. Twenty-three healthy subjects 
were recruited, and 42 consecutive patients with CVD (e.g., heart failure, cardiomyopathy, or coronary artery 
disease) who underwent incremental exercise testing between November 2019 and November 2020 at Keio 
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Figure 5.  Fabrication and function of the sweat lactate sensor chip. (A) Parts composition of the lactate acid 
sensor chip. (B) Schematic diagram of the reagent layer and processes involved in the amperometric sensing of 
lactate acid on the working electrode. (C) The parts composition of device. (1) Bluetooth LE System-on-Chip: 
Taiyo Yuden EYSHCNZWZ, (2) Potentiostat unit: Texas Instruments DAC081C085 (DAC), Microchip Technology 
MCP6041T-I/OT and MCP6042T-I/MS (OP AMP), (3) temperature sensor: Ablic S-5851A, (4) flash memory: 
Winbond Electronics W25Q32JV(4 MB), (5) power unit: Texas Instruments BQ24232RGTR (Charger IC), Synergy 
ScienTech AHB512229PR (Li-ion Battery: 3.7 V, 295 mAh) and ON Semiconductor LC709203F (fuel gauge).
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University Hospital were enrolled. The healthy subjects had a broad spectrum of aerobic capacities and fitness 
levels, but were not athletes, and had no comorbidities, such as hypertension, diabetes, or active lung diseases. 
Exclusion criteria for the patients with CVD included 2 or 3 degree-conduction block without a cardiac implant-
able electronic device, severe pulmonary hypertension, decompensated heart failure, more than severe primary 
valvular heart diseases, and an acute phase of the acute coronary syndrome. The study protocol was approved by 
the Institutional Review Board of Keio University School of Medicine [permission number; 2014023, 20180357], 
and was conducted in accordance with the Declaration of Helsinki. All subjects provided written informed 
consent.
Experimental procedure. 
The exercise tests were performed with the RAMP protocol ergometer in both 
healthy subjects and patients with CVD, simultaneously monitoring the changes in sweat lactate with a wear-
able lactate sensor. In all healthy subjects, the sensor was attached to the upper arm, and lactates in blood were 
measured every 2 min. Conversely, in patients with CVD, the sensor was attached to the forehead which has 
a higher-sweat rate compared to the upper arm as they were less likely to sweat than healthy subjects. All the 
patients underwent an exercise test with respiratory gas analysis. Among the 42 patients, 17 patients refused the 
blood lactate test during exercise because of the invasive procedure. In only 25 patients who provided written 
informed consent, blood lactate concentrations were measured during the exercise  test26,33.
Exercise testing protocol. 
On the day of the exercise test, the subjects avoided heavy physical activity 
before the test. The subjects performed the test in the upright position on an electronically braked ergometer 
(STRENGTH ERGO 8, Mitsubishi Electric Engineering Company, Japan). Following a 2-min rest to stabilize 
the heart rate and respiratory condition, the subjects performed a 2-min warm-up pedaling at 50 W for healthy 
men and at 0 W for healthy women and patients, and then exercised with a progressive intensity until the sub-
jects could no longer maintain the pedaling rate (volitional exhaustion). At 1-min intervals, the intensity was 
increased by 20 W increments for healthy subjects, and 10 or 15 W increments for CVD patients (RAMP pro-
tocol). The pedaling frequency was set at 60 rev/min. The incremental exercise testing time ranged from 10 to 
20 min, depending on the exercise capacities of each subject or patient. Once the exercise tests were terminated 
the subjects were instructed to stop pedaling and to stay on the ergometer for 3  min26,33.
Respiratory gas analysis and Ventilatory threshold. 
The additional method is available in the sup-
plemental material S1. The expired gas flows were measured using a breath-by-breath automated system (AERO-
MONITOR, MINATO MedicalScience CO., LTD., Osaka, Japan). The respiratory gas exchange, including ven-
tilation (VE), oxygen uptake  (VO2), and carbon dioxide production  (VCO2), was continuously monitored and 
measured using a 10-s average. VT1 was determined using the ventilatory equivalent, excess  CO2, and modified 
V-slope  methods8. Three exercise testing experts, agreed on the VT1, independently from those who determined 
the sLT. First, two of three experienced researchers independently and randomly evaluated the VT1 of each 
subject using the three methods. The researchers used all three methods to assess concurrent break point and 
to eliminate false breakpoint. Second, if the  VO2 values determined by the independent researchers were within 
3%, then the  VO2 values for 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 then independently determined 
 VO2. The third  VO2 value was then compared to those obtained by the initial investigators. If the adjudicated 
 VO2 value was within 3% of either of the initial investigators, then two  VO2 values were  averaged26,33.
Lactate threshold in blood. 
The blood lactate values were obtained via auricular pricking and squeezing 
the ear lobe gently to obtain a capillary blood sample every 2 min during the exercise test. The samples were 
analyzed immediately for the whole blood lactate concentration (mmol/L) using a standard enzymatic method 
on a lactate analyzer (LACTATE PRO2, ARKRAY, Japan)34.
The bLT was determined through graphical  plots35. A visual interpretation was independently made of each 
subject by two experienced researchers to locate the first rise from baseline. If the independent determinations 
of the stage at LT1 differed between the two researchers, a third researcher adjudicated the difference by inde-
pendently determining LT1. The three researchers then jointly agreed on the LT1 point.
Lactate threshold in sweat. 
The sLT was defined as the first significant increase in lactate in sweat above 
the baseline based on the graphical plots and Change Finder scores calculated by Change Finder algorithm 
(Online Fig. S3). Several candidate points (change points) of sLT were extracted by applying Change Finder 
 algorithm36 to the time-series data of the lactate values in sweat in the range from the start to the end of exer-
cise. Two-step learning with a Sequentially Discounting AR (SDAR) model was used to accurately distinguish 
between outliers and change points in the Change Finder algorithm. Three researchers, independently of the 
researchers who analyzed respiratory gas exchange, jointly agreed on the point of sLT.
Statistical analyses. 
The results are represented as median with an interquartile range (IQR) for con-
tinuous variables and as percentages for categorical variables, as appropriate. A univariable logistic regression 
analysis was performed to estimate the adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for non-
response in the lactate sensor. The relationships among the work rate (WR) at the sLT, bLT, and VT1 were inves-
tigated using the Pearson’s correlation coefficient test. Additionally, the Bland and Altman technique was applied 
to verify the similarities among the different  methods37. This comparison was a graphical representation of the 
difference between the methods and the average of these methods. Further, ordinary least products regression 
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analysis was used to evaluate the fixed and proportional biases between each  threshold38,39. All probability values 
were 2-tailed with P values < 0.05 considered statistically significant. All statistical analyses were performed with 
R version 3.6.3 (R Core Team, 2020, R: A language and environment for statistical computing. R Foundation 
for Statistical Computing, Vienna, Austria).
Received: 2 September 2020; Accepted: 9 February 2021
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Acknowledgements
The authors thank M. Fujioka, C. Yoshida, K. Takeuchi, and R. Kendo for their technical assistance. We are 
grateful to Editage for editing this manuscript.
Author contributions
The author contributions are stated as follows; Y.S., D.N. and Y.K drew the manuscript. Y.S., D.N., M.F., M.S. and 
Y.K. prepared the images. Y.S., D.N., Y.S., T.R., H.I., K.M. and Y.K. collected the patient information. T.W., T.N., 
M.M., M.N., K.S., K.F. and Y.K. provided a critical revision of the manuscript for the key intellectual content 
and supervision. All of the authors have approved all aspects of our work, read, and approved the manuscript.
Competing interests 
This study was funded by Grant-in-Aid from Scientific Research from the Japan Agency for Medical Research 
and Development (ID. 19ek0210130h0001) and by a grant from 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, decision to 
publish or preparation of the manuscript. D.N is a founder and shareholder of Grace imaging Inc. Y. Shiraishi 
is affiliated with a department endowed by Nippon Shinyaku Co., Ltd., and received a research grant from the 
SECOM Science and Technology Foundation and an honorarium from Otsuka Pharmaceutical Co., Ltd. M. S. 
is an employee of Grace imaging Inc.
Additional information
Supplementary Information The online version contains supplementary material available at https ://doi.
org/10.1038/s4159 8-021-84381 -9.
Correspondence and requests for materials should be addressed to D.N. or Y.K.
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© The Author(s) 2021

A novel device for detecting anaerobic threshold using sweat lactate during exercise.
Seki, Yuta,Nakashima, Daisuke,Shiraishi, Yasuyuki,Ryuzaki, Toshinobu,Ikura, Hidehiko,Miura, Kotaro,Suzuki, Masato,Watanabe, Takatomo,Nagura, Takeo,Matsumato, Morio,Nakamura, Masaya,Sato, Kazuki,Fukuda, Keiichi,Katsumata, Yoshinori