Citation: Maeda, Y.; Okawara, H.;
Sawada, T.; Nakashima, D.;
Nagahara, J.; Fujitsuka, H.; Ikeda, K.;
Hoshino, S.; Kobari, Y.; Katsumata, Y.;
et al. Implications of the Onset of
Sweating on the Sweat Lactate
Threshold. Sensors 2023, 23, 3378.
https://doi.org/10.3390/s23073378
Academic Editors:
Abdelhamid Errachid, Maria
Giovanna Trivella and Francesca
G. Bellagambi
Received: 8 February 2023
Revised: 5 March 2023
Accepted: 20 March 2023
Published: 23 March 2023
Copyright:
© 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sensors
Article
Implications of the Onset of Sweating on the Sweat
Lactate Threshold
Yuta Maeda 1
, Hiroki Okawara 1
, Tomonori Sawada 1
, Daisuke Nakashima 1,*
, Joji Nagahara 1,
Haruki Fujitsuka 1, Kaito Ikeda 1, Sosuke Hoshino 1, Yusuke Kobari 2, Yoshinori Katsumata 2,3,*
,
Masaya Nakamura 1
and Takeo Nagura 1,4
1
Department of Orthopaedic Surgery, Keio University School of Medicine, Shinjuku, Tokyo 160-8582, Japan
2
Department of Cardiology, Keio University School of Medicine, Shinjuku, Tokyo 160-8582, Japan
3
Institute for Integrated Sports Medicine, Keio University School of Medicine, Shinjuku, Tokyo 160-8582, Japan
4
Department of Clinical Biomechanics, Keio University School of Medicine, Shinjuku, Tokyo 160-8582, Japan
*
Correspondence: nakashima@keio.jp (D.N.); goodcentury21@keio.jp (Y.K.);
Tel.: +81-3-5363-3812 (D.N.); +81-3-3353-1211 (Y.K.)
Abstract: The relationship between the onset of sweating (OS) and sweat lactate threshold (sLT)
assessed using a novel sweat lactate sensor remains unclear. We aimed to investigate the implications
of the OS on the sLT. Forty healthy men performed an incremental cycling test. We monitored the
sweat lactate, blood lactate, and local sweating rates to determine the sLT, blood LT (bLT), and OS.
We deﬁned participants with the OS during the warm-up just before the incremental test as the early
perspiration (EP) group and the others as the regular perspiration (RP) group. Pearson’s correlation
coefﬁcient analysis revealed that the OS was poorly correlated with the sLT, particularly in the EP
group (EP group, r = 0.12; RP group, r = 0.56). Conversely, even in the EP group, the sLT was strongly
correlated with the bLT (r = 0.74); this was also the case in the RP group (r = 0.61). Bland-Altman
plots showed no bias between the mean sLT and bLT (mean difference: 19.3 s). Finally, in ﬁve cases
with a later OS than bLT, the sLT tended to deviate from the bLT (mean difference, 106.8 s). The sLT is
a noninvasive and continuous alternative to the bLT, independent of an early OS, although a late OS
may negatively affect the sLT.
Keywords: lactate threshold; sweat rate; exercise testing; incremental exercise; sweating; perspiration;
body temperature regulation; sports; physiology
1. Introduction
The visualization of exercise tolerance to optimize daily training is encouraged by
athletes and supporters. In particular, monitoring metabolic responses, such as the anaer-
obic threshold (AT), during exercise enables athletes to evaluate their aerobic capacity
in real-time [1–4], which leads to a deﬁned relative workload intensity [4,5]. Previous
studies have investigated the ventilatory threshold (VT), measured using an expiration gas
analyzer, and the lactate threshold (LT) as good indicators of AT [3,6]. VT testing is costly
and requires expertise; thus, it is not easily accessible to recreational or younger athletes
without favorable facilities. LT testing requires frequent blood sampling while interrupting
strenuous exercise, meaning it does not reﬂect usual (continuous) exercise. In addition,
contamination with other substances, such as sweat, makes the assessment difﬁcult.
In recent years, wearable sensing technology has been the focus of more precise
evaluations of physiological responses in the body. We have developed a method to
visualize the lactate dynamics of sweat during exercise in a noninvasive, simple, and
real-time manner [7,8]. Furthermore, the sweat LT (sLT), assessed using sweat lactate
dynamics, is consistent with the LT calculated from blood samples (bLT) and the VT [7].
However, unlike blood lactate levels, changes in sweat lactate levels may be affected by
sweating dynamics.
Sensors 2023, 23, 3378. https://doi.org/10.3390/s23073378
https://www.mdpi.com/journal/sensors
Sensors 2023, 23, 3378
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During exercise, sweating occurs with a rise in body temperature that reﬂects the
increased workload [9–11]. Previous reports have shown a negative correlation between
the local sweating rate and sweat lactate level during exercise [12–14], suggesting that
increased sweating dilutes the sweat lactate concentration. However, the relationship
between the onset of sweating (OS) and sLT remains unclear. If the relationship between
the bLT and sLT is strongly dependent on the OS, LT determination using sweat instead of
blood may require careful attention to environmental conditions, such as the temperature
and humidity, based on the subject’s condition. We aimed to investigate the relationship
between the OS and sLT and the effect of the OS on the relationship between the sLT and
bLT during incremental exercise.
2. Materials and Methods
2.1. Participants
Volunteers were recruited from December 2020 to August 2021, and 40 healthy men
aged 18–37 years participated in this study. The exclusion criteria were as follows: (1) the
presence of comorbidities such as active cardiopulmonary disease, hypertension, or di-
abetes within two weeks; (2) elite athletes; (3) smokers and those taking medication or
performance-enhancing drugs.
The study protocol was conducted in compliance with the ethical guidelines for
medical and health research involving human subjects and was approved by the Ethics
Committees of Keio University School of Medicine (Approval No. 20180357). Written
informed consent was obtained from the study participants for the participation and
publication of the ﬁndings before enrollment.
2.2. Procedures and Protocols
All experiments were performed at sports facilities under similar conditions (24.8 ± 1.9 ◦C
temperature, 42.0 ± 9.3% relative humidity). Prior to the exercise test, the body weight
(kg) and height (cm) of each participant were measured. The body mass index (BMI) and
body surface area (BSA) were calculated using the formula used by DuBois (body surface
area (m2) = 0.007184 × height (cm)0.725 × weight (kg)0.425) [15]. The participants were
instructed to avoid drinking alcohol or caffeine for 12 h [16]. They were also required to be
well hydrated and refrain from vigorous exercise for 3 h before the exercise test.
Each participant completed incremental exercise using an electromagnetically braked
ergometer (POWER MAX V3 Pro; Konami Sports Co., Ltd., Tokyo, Japan). The ergometer
seat was adjusted to a favorable height. Two minutes of rest was set to measure the resting
outcomes. Immediately after the warm-up for 4 min with a load of 20 W, the exercise test
was started at 50 W, increasing by 25 W every minute. The pedaling cadence range was set
to 70–80 revolutions per minute (rpm). Each test was terminated owing to (a) subjective
exhaustion or (b) reaching 70 rpm for 10 s.
2.3. Measurements during the Exercise Test
The sweat rate and sweat lactate concentration were continuously monitored during
incremental exercise. The sweat lactate was measured using a sweat lactate sensor chip
(Grace Imaging Inc., Tokyo, Japan), which we developed and applied in several studies
(Figure S1) [7,8,17,18]. It is a type of electrochemical sensor that detects the potential
generated by the redox reaction between the lactate and lactate oxidase. The special
protective membrane structure of the sensor allows the aforementioned reaction to last for
30 to 60 min. As a result, the lactate concentration in sweat can be continuously measured
throughout the exercise test. Further detailed information regarding the composition
and fabrication of the sensor chip is available in our previous study [7]. This sweat
lactate sensor chip connected to a wearable sweat lactate sensor was placed on the upper
arm [7]. The installation area was carefully cleaned using an alcohol-free cloth to prevent
contamination of the sweat sample. The sensor chip was ﬁrmly ﬁxed with tape to prevent
detachment from the skin. Real-time sweat lactate values were automatically recorded in
Sensors 2023, 23, 3378
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a connected application device (Grace Imaging Inc., Tokyo, Japan) via Bluetooth at 1 Hz.
The sweat lactate value was quantiﬁed as the current value because the chip reacts with
sweat lactate and generates an electric current [7,8,18,19]. The sLT was deﬁned as the
ﬁrst signiﬁcant increase in the lactate concentration in sweat above the baseline based on
graphical plots [7,8,18,19].
The sweat rate was measured using a pre-calibrated perspiration meter (SKN-200M;
SKINOS Co., Ltd., Ueda, Japan) on the upper arm [17] and a Fitbit Inspire HR (Fitbit Inc.,
San Francisco, CA, USA) was attached to the left wrist, two-ﬁnger widths above the ulnar
styloid process, to measure the heart rate. The heart rate at rest was measured once at the
onset of the warm-up. The sweat rate was recorded at 1 Hz and expressed in milligrams
per square centimeter per minute (mg/cm2/min). The baseline of the local sweat rate was
calculated as the average of the sweat volume in the rest period. The OS was deﬁned as the
ﬁrst signiﬁcant increase in sweat rate above the baseline based on graphical plots (Figure 1).
 
y
measured throughout the exercise test. Further detailed information regarding the 
composition and fabrication of the sensor chip is available in our previous study [7]. This 
sweat lactate sensor chip connected to a wearable sweat lactate sensor was placed on the 
upper arm [7]. The installation area was carefully cleaned using an alcohol-free cloth to 
prevent contamination of the sweat sample. The sensor chip was ﬁrmly ﬁxed with tape to 
prevent detachment from the skin. Real-time sweat lactate values were automatically 
recorded in a connected application device (Grace Imaging Inc., Tokyo, Japan) via 
Bluetooth at 1 Hz. The sweat lactate value was quantiﬁed as the current value because the 
chip reacts with sweat lactate and generates an electric current [7,8,18,19]. The sLT was 
deﬁned as the ﬁrst signiﬁcant increase in the lactate concentration in sweat above the 
baseline based on graphical plots [7,8,18,19]. 
The sweat rate was measured using a pre-calibrated perspiration meter (SKN-200M; 
SKINOS Co., Ltd., Ueda, Japan) on the upper arm [17] and a Fitbit Inspire HR (Fitbit Inc., 
San Francisco, CA, USA) was attached to the left wrist, two-ﬁnger widths above the ulnar 
styloid process, to measure the heart rate. The heart rate at rest was measured once at the 
onset of the warm-up. The sweat rate was recorded at 1 Hz and expressed in milligrams 
per square centimeter per minute (mg/cm2/min). The baseline of the local sweat rate was 
calculated as the average of the sweat volume in the rest period. The OS was deﬁned as 
the ﬁrst signiﬁcant increase in sweat rate above the baseline based on graphical plots 
(Figure 1). 
 
Figure 1. Representative data of the local sweat rate, blood lactate, and sweat lactate throughout the 
exercise protocol, including the rest and warm-up periods. The onset of sweating (OS), blood lactate 
threshold (bLT), and sweat lactate threshold (sLT) are indicated by the dotted lines. The OS preceded 
both the bLT and sLT (35 out of 40 cases, 88%). 
To measure blood lactate concentration, a blood sample was obtained from the 
auricle at rest and every minute during exercise. The lactate concentration in the blood 
was immediately measured using a lactate analyzer (Lactate Pro2 LT-1730, ARKRAY, Inc., 
Kyoto, Japan). The blood lactate values were graphically plotted in millimoles per liter 
(mmol/L). The bLT was determined using graphical plots [3]. 
Figure 1. Representative data of the local sweat rate, blood lactate, and sweat lactate throughout the
exercise protocol, including the rest and warm-up periods. The onset of sweating (OS), blood lactate
threshold (bLT), and sweat lactate threshold (sLT) are indicated by the dotted lines. The OS preceded
both the bLT and sLT (35 out of 40 cases, 88%).
To measure blood lactate concentration, a blood sample was obtained from the auricle
at rest and every minute during exercise. The lactate concentration in the blood was
immediately measured using a lactate analyzer (Lactate Pro2 LT-1730, ARKRAY, Inc.,
Kyoto, Japan). The blood lactate values were graphically plotted in millimoles per liter
(mmol/L). The bLT was determined using graphical plots [3].
2.4. Statistical Analysis
The OS, bLT, and sLT were determined visually by three researchers independently
in accordance with previous reports [7,17]. We ﬁrst deﬁned subjects with the OS during
the resting period or warm-up as the early perspiration (EP) group, and those with the
OS during incremental exercise as the regular perspiration (RP) group. The relationships
between the OS, sLT, and bLT were examined using Pearson’s correlation coefﬁcient and a
linear regression analysis. For each combination, a one-sample t-test on the difference in
time was used to examine the ﬁxed error, and a linear regression analysis on the mean and
Sensors 2023, 23, 3378
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difference in time was used to rule out the proportional error. Furthermore, to investigate
the effect of an early OS on the approximation between the bLT and sLT, we compared mean
bLT–sLT (s) values between the EP and RP groups using an independent t-test. Based on
the results of the Kolmogorov–Smirnov test, either an independent t-test or Mann–Whitney
U test was applied to the age, height, weight, BMI, body water, BSA, room temperature,
and relative humidity to compare each variable between the groups. All analyses were
performed using IBM SPSS Statistics version 27 (IBM Corp., Armonk, NY, USA), with the
statistical signiﬁcance set at 0.05.
3. Results
3.1. Participants Characteristics and Physiological Results
All participants completed all procedures and were eligible for the analysis. During
exercise, continuous negligible responses were initially detected in the sweat rate, sweat
lactate, and blood lactate (Figure 1). Subsequently, they rapidly increased and were deﬁned
as the OS, sLT, and bLT, respectively. For all participants, these time points could be
clearly deﬁned. Based on the relationship between these points, 17 participants (43%) were
categorized into the EP group and 23 participants (57%) into the RP group. Furthermore, in
ﬁve participants (13%), the OS was later than the bLT.
The descriptive data of the participants in the EP and RP groups are presented in
Table 1. No signiﬁcant differences were observed in any of the characteristics between the
groups (p > 0.05).
Table 1. Descriptive data of the participants.
Total (n = 40)
Early Perspiration
Group (n = 17)
Regular Perspiration
Group (n = 23)
p-Value
Age (years)
21.8 ± 4.0
22.4 ± 4.6
21.4 ± 3.5
0.71
BMI
22.4 ± 2.1
22.7 ± 1.9
22.2 ± 2.3
0.48
BSA (m2)
1.8 ± 0.1
1.8 ± 0.1
1.8 ± 0.1
0.86
Body water (%)
57.8 ± 5.4
56.8 ± 5.6
58.6 ± 5.3
0.31
Body fat ratio (%)
17.1 ± 5.0
18.6 ± 5.4
16.0 ± 4.5
0.11
Muscle mass (kg)
53.6 ± 5.8
53.0 ± 5.3
54.0 ± 6.3
0.61
RT (◦C)
24.1 ± 1.9
24.2 ± 1.8
24.0 ± 2.0
0.91
RH (%)
42.5 ± 9.3
42.3 ± 8.6
42.6 ± 9.9
0.92
All values are presented as means ± standard deviations or n (%). Based on the pre-performed Kolmogorov–
Smirnov test, the independent t-test or Mann–Whitney U test was applied for intra-group comparisons. BSA,
body surface area; RT, room temperature; RH, relative humidity.
Table 2 shows the physiological results including the load, heart rate, sweat lactate
level, and local sweat rate. As in previous reports, the heart rate increased with the exercise
intensity and the sweat rate increased after the onset of the incremental exercise. The sweat
lactate levels were stable at ﬁrst and then rapidly increased from the sLT to the end.
Table 2. Physiological data of the participants.
Baseline
At the Warm-Up
Onset
At the Incremental
Exercise Onset
At the Sweat
Lactate Threshold
At the End of
Incremental Exercise
Load (watt)
0.0 ± 0.0
20.0 ± 0.0
50.0 ± 0.0
131.7 ± 48.5
261.2 ± 43.6
Heart rate (bpm)
-
79.3 ± 11.1
94.4 ± 14.0
137.0 ± 23.1
172.9 ± 13.8
Sweat lactate (µA)
4.0 ± 1.1
3.9 ± 1.2
3.7 ± 1.2
3.9 ± 1.4
9.6 ± 4.6
Local sweat rate
(mg/cm2/min)
0.04 ± 0.11
0.01 ± 0.09
0.08 ± 0.17
0.18 ± 0.20
0.84 ± 0.46
The baseline is the average of data in the rest period. All values are presented as means ± standard deviations.
Sensors 2023, 23, 3378
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3.2. Relationship between OS and sLT
Figure 2a shows the relationship between the OS and sLT. A poor correlation was
observed in the total cohort (r = 0.38, p < 0.05), and no correlation was observed in the
EP group (r = 0.12, p = 0.61). In contrast, in the RP group, the OS moderately correlated
with the sLT (r = 0.56, p < 0.05). In the ﬁve patients with the OS later than the bLT, the
OS showed a strong correlation with the sLT (r = 0.82, p = 0.09). The Bland-Altman plot
revealed that that the mean difference between the OS and sLT was large in total, especially
in the EP group (total, 183.7 s, EP group, 389.6 s, RP group, 85.2 s), which validates the
inconsistency between these thresholds (Figure 2b). Figure 2b also shows a positive ﬁxed
error (p < 0.05, 95% CI: 155.1–274.1) and a proportional error (p < 0.05) in total. The ﬁxed
error indicated that the sLT occurred after the OS in all the cases.
 
Heart rate (bpm) 
- 
79.3 ± 11.1 
94.4 ± 14.0 
137.0 ± 23.1 
172.9 ± 13.8 
Sweat lactate (µA) 
4.0 ± 1.1 
3.9 ± 1.2 
3.7 ± 1.2 
3.9 ± 1.4 
9.6 ± 4.6 
Local sweat rate 
(mg/cm2/min) 
0.04 ± 0.11 
0.01 ± 0.09 
0.08 ± 0.17 
0.18 ± 0.20 
0.84 ± 0.46 
The baseline is the average of data in the rest period. All values are presented as means ± standard 
deviations. 
3.2. Relationship between OS and sLT 
Figure 2a shows the relationship between the OS and sLT. A poor correlation was 
observed in the total cohort (r = 0.38, p < 0.05), and no correlation was observed in the EP 
group (r = 0.12, p = 0.61). In contrast, in the RP group, the OS moderately correlated with 
the sLT (r = 0.56, p < 0.05). In the ﬁve patients with the OS later than the bLT, the OS showed 
a strong correlation with the sLT (r = 0.82, p = 0.09). The Bland-Altman plot revealed that 
that the mean diﬀerence between the OS and sLT was large in total, especially in the EP 
group (total, 183.7 s, EP group, 389.6 s, RP group, 85.2 s), which validates the inconsistency 
between these thresholds (Figure 2b). Figure 2b also shows a positive ﬁxed error (p < 0.05, 
95% CI: 155.1–274.1) and a proportional error (p < 0.05) in total. The ﬁxed error indicated 
that the sLT occurred after the OS in all the cases. 
 
Figure 2. The relationship between the onset of sweating and sweat lactate threshold. (a) A scatter 
plot and approximation line between the onset of sweating (OS) and the sweat lactate threshold 
(sLT). The correlation was minimal in the total cohort and early perspiration group (EP), while that 
in the regular perspiration group (RP) was moderate. (b) Validity testing using Bland-Altman plots, 
which indicated the respective diﬀerences between the time at the OS and sLT (y-axis) against the 
mean time of OS and sLT (x-axis). Red triangles indicate the EP group data and circles indicate the 
RP group data. Note: r, correlation coeﬃcient; ULOA, upper limit of agreement (=mean + 1.96 × 
standard deviation); LLOA, lower limit of agreement (= mean − 1.96 × standard deviation). 
3.3. Relationship between OS and Blood LT 
Figure 3a shows the relationship between the OS and bLT. There was no correlation 
in the total cohort (r = 0.23, p = 0.16), EP (r = −0.06, p = 0.83) or RP groups (r = 0.30, p = 0.16). 
The Bland-Altman plot revealed that the mean diﬀerence between the OS and sLT was 
large in total, especially in the EP group (total, −195.3 s; EP group, −381.6 s; RP group, 
Figure 2. The relationship between the onset of sweating and sweat lactate threshold. (a) A scatter
plot and approximation line between the onset of sweating (OS) and the sweat lactate threshold (sLT).
The correlation was minimal in the total cohort and early perspiration group (EP), while that in the
regular perspiration group (RP) was moderate. (b) Validity testing using Bland-Altman plots, which
indicated the respective differences between the time at the OS and sLT (y-axis) against the mean time
of OS and sLT (x-axis). Red triangles indicate the EP group data and circles indicate the RP group
data. Note: r, correlation coefﬁcient; ULOA, upper limit of agreement (=mean + 1.96 × standard
deviation); LLOA, lower limit of agreement (=mean − 1.96 × standard deviation).
3.3. Relationship between OS and Blood LT
Figure 3a shows the relationship between the OS and bLT. There was no correlation in
the total cohort (r = 0.23, p = 0.16), EP (r = −0.06, p = 0.83) or RP groups (r = 0.30, p = 0.16).
The Bland-Altman plot revealed that the mean difference between the OS and sLT was
large in total, especially in the EP group (total, −195.3 s; EP group, −381.6 s; RP group,
−57.6 s), which validates the inconsistency between those thresholds (Figure 3b). Figure 3b
also shows both a positive ﬁxed error (p < 0.05, 95% CI: 132.1–258.6) and a proportional
error (p < 0.05) in total. The negative ﬁxed error indicated that the bLT tended to come after
the OS, while the bLT preceded the OS in ﬁve cases (out of 23 cases, 21%) in the RP group.
3.4. Effect of Early OS on the Blood–Sweat Lactate Threshold Approximation
As shown in Figure 4a, the sLT was strongly correlated with the bLT, regardless of
early perspiration (total, r = 0.68, p < 0.01; EP group, r = 0.74, p < 0.01; RP group, r = 0.61,
p < 0.01). The linear regression analysis revealed that the sLT can be a good indicator of the
bLT (total, y = 252.9 + 0.54x, p < 0.01, R = 0.68; EP group, y = 217.9 + 0.60x, p < 0.01, R = 0.74;
RP group, y = 289.1 + 0.49x, p < 0.01, R = 0.61). The Bland-Altman plot showed no bias
Sensors 2023, 23, 3378
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between the bLT and sLT in each group (total, 19.3 s; EP group, 8.0 s; RP group, 27.6 s), and
validated a strong agreement between those thresholds (Figure 4b). Figure 4b also shows
that in the ﬁve cases where the OS occurred later than the bLT (described in blue circle),
the sLT was more likely to deviate from the bLT than in other cases. The mean difference
between the sLT and bLT among the ﬁve was 106.8 s. There was no ﬁxed error (p > 0.05,
95% CI: −4.8 to 43.3) or proportional error (p > 0.05) in the total group.
Sensors 2023, 23, x FOR PEER REVIEW 
6 of 11 
 
 
−57.6 s), which validates the inconsistency between those thresholds (Figure 3b). Figure 
3b also shows both a positive ﬁxed error (p < 0.05, 95% CI: 132.1–258.6) and a proportional 
error (p < 0.05) in total. The negative ﬁxed error indicated that the bLT tended to come 
after the OS, while the bLT preceded the OS in ﬁve cases (out of 23 cases, 21%) in the RP 
group. 
 
Figure 3. The relationship between the onset of sweating and blood lactate threshold. (a) Scatter plot 
and approximation line between the OS and bLT. No signiﬁcant correlation was found in the total, 
early perspiration group (EP), or regular perspiration (RP) groups. (b) Validity testing using Bland-
Altman plots, which indicated the respective diﬀerence between the time at the OS and bLT (y-axis) 
against the mean time of the OS and bLT (x-axis). Triangles indicate the EP group data and circles 
indicate the RP group data. Note: r, correlation coeﬃcient; ULOA, upper limit of agreement (= mean 
+ 1.96 × standard deviation); LLOA, lower limit of agreement (= mean − 1.96 × standard deviation). 
3.4. Eﬀect of Early OS on the Blood–Sweat Lactate Threshold Approximation 
As shown in Figure 4a, the sLT was strongly correlated with the bLT, regardless of 
early perspiration (total, r = 0.68, p < 0.01; EP group, r = 0.74, p < 0.01; RP group, r = 0.61, p 
< 0.01). The linear regression analysis revealed that the sLT can be a good indicator of the 
bLT (total, y = 252.9 + 0.54x, p < 0.01, R = 0.68; EP group, y = 217.9 + 0.60x, p < 0.01, R = 0.74; 
RP group, y = 289.1 + 0.49x, p < 0.01, R = 0.61). The Bland-Altman plot showed no bias 
between the bLT and sLT in each group (total, 19.3 s; EP group, 8.0 s; RP group, 27.6 s), 
and validated a strong agreement between those thresholds (Figure 4b). Figure 4b also 
shows that in the ﬁve cases where the OS occurred later than the bLT (described in blue 
circle), the sLT was more likely to deviate from the bLT than in other cases. The mean 
diﬀerence between the sLT and bLT among the ﬁve was 106.8 s. There was no ﬁxed error 
(p > 0.05, 95% CI: −4.8 to 43.3) or proportional error (p > 0.05) in the total group. 
Figure 3. The relationship between the onset of sweating and blood lactate threshold. (a) Scatter plot
and approximation line between the OS and bLT. No signiﬁcant correlation was found in the total,
early perspiration group (EP), or regular perspiration (RP) groups. (b) Validity testing using Bland-
Altman plots, which indicated the respective difference between the time at the OS and bLT (y-axis)
against the mean time of the OS and bLT (x-axis). Triangles indicate the EP group data and circles
indicate the RP group data. Note: r, correlation coefﬁcient; ULOA, upper limit of agreement (=mean
+ 1.96 × standard deviation); LLOA, lower limit of agreement (=mean − 1.96 × standard deviation).
Sensors 2023, 23, x FOR PEER REVIEW 
7 of 11 
 
 
Figure 4. The relationship between the sweat and blood lactate thresholds. (a) Scatter plot and 
approximation line between the sLT and bLT. Signiﬁcant correlations were found in the total, early 
perspiration (EP), and regular perspiration (RP) groups. The linear regression analysis also 
indicated a strong eﬀect of the sLT on the bLT estimation in each group. (b) Validity testing using 
Bland-Altman plots, which indicated the respective diﬀerence between the time at the bLT and sLT 
(y-axis) against the mean time at the sLT and bLT (x-axis). Red triangles indicate the EP group data, 
black or blue circles indicate the RP group data. Among the RP group data, blue circles indicate the 
late perspiration data, where the time of the bLT preceded the time of the OS. Note: r, correlation 
coeﬃcient; R; multiple correlation coeﬃcient; ULOA, upper limit of agreement (= mean + 1.96 × 
standard deviation); LLOA, lower limit of agreement (= mean − 1.96 × standard deviation). 
Figure 5 also shows that there is no signiﬁcant diﬀerence between the bLT–sLT 
approximations (= sLT–bLT (s)) in the EP and RP groups (p > 0 05)
Figure 4. The relationship between the sweat and blood lactate thresholds. (a) Scatter plot and
approximation line between the sLT and bLT. Signiﬁcant correlations were found in the total, early
perspiration (EP), and regular perspiration (RP) groups. The linear regression analysis also indicated
a strong effect of the sLT on the bLT estimation in each group. (b) Validity testing using Bland-Altman
plots, which indicated the respective difference between the time at the bLT and sLT (y-axis) against
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the mean time at the sLT and bLT (x-axis). Red triangles indicate the EP group data, black or blue
circles indicate the RP group data. Among the RP group data, blue circles indicate the late perspiration
data, where the time of the bLT preceded the time of the OS. Note: r, correlation coefﬁcient; R; multiple
correlation coefﬁcient; ULOA, upper limit of agreement (=mean + 1.96 × standard deviation); LLOA,
lower limit of agreement (=mean − 1.96 × standard deviation).
Figure 5 also shows that there is no signiﬁcant difference between the bLT–sLT approx-
imations (=sLT–bLT (s)) in the EP and RP groups (p > 0.05).
 
 
Figure 4. The relationship between the sweat and blood lactate thresholds. (a) Scatter plot and 
approximation line between the sLT and bLT. Signiﬁcant correlations were found in the total, early 
perspiration (EP), and regular perspiration (RP) groups. The linear regression analysis also 
indicated a strong eﬀect of the sLT on the bLT estimation in each group. (b) Validity testing using 
Bland-Altman plots, which indicated the respective diﬀerence between the time at the bLT and sLT 
(y-axis) against the mean time at the sLT and bLT (x-axis). Red triangles indicate the EP group data, 
black or blue circles indicate the RP group data. Among the RP group data, blue circles indicate the 
late perspiration data, where the time of the bLT preceded the time of the OS. Note: r, correlation 
coeﬃcient; R; multiple correlation coeﬃcient; ULOA, upper limit of agreement (= mean + 1.96 × 
standard deviation); LLOA, lower limit of agreement (= mean − 1.96 × standard deviation). 
Figure 5 also shows that there is no signiﬁcant diﬀerence between the bLT–sLT 
approximations (= sLT–bLT (s)) in the EP and RP groups (p > 0.05). 
 
Figure 5. Comparison of the approximations of the sweat lactate threshold to the blood lactate 
threshold. Box plot of the diﬀerence between the times at the blood and sweat lactate thresholds. 
Black circles indicate the respective data. No signiﬁcant diﬀerences were observed between the early 
perspiration and regular perspiration groups (p = 0.42). Note: n.s., not signiﬁcance (p > 0.05). 
 
 
Figure 5. Comparison of the approximations of the sweat lactate threshold to the blood lactate
threshold. Box plot of the difference between the times at the blood and sweat lactate thresholds.
Black circles indicate the respective data. No signiﬁcant differences were observed between the early
perspiration and regular perspiration groups (p = 0.42). Note: n.s., not signiﬁcance (p > 0.05).
4. Discussion
The most striking ﬁnding of this study was the poor association between the OS and
sLT during incremental exercises. Moreover, the sLT was strongly correlated with the bLT,
independent of the preceding OS. These ﬁndings provide further information regarding LT
measurements of sweat lactate as a noninvasive, continuous, real-time analytical alternative
to blood lactate testing.
The analysis of sweat, which contains various types of physiological information [20],
has attracted the attention of researchers and physiologists in the athletic ﬁeld. In particular,
the lactate in sweat is concentrated, and the sLT is reported to correlate with VT and bLT [7],
which are common indicators of the aerobic exercise capacity [1–6]. However, the relation-
ship between the sweat kinetics and sweat lactate kinetics is yet to be fully investigated;
therefore, the interpretation of sweat lactate concentrations remains controversial.
We focused on the effect of an early OS on the time of the sLT by monitoring the local
sweating rate and sweat lactate concentration. Herein, no correlation was observed between
the OS and sLT in the EP and RP groups. This implies that the sLT is regulated independent
of the OS and that the sweat lactate dynamics are not necessarily consistent with the
sweat dynamics. Sweating occurs due to an increase in core body temperature [9], and the
threshold temperature does not change with exercise [21–23] or exercise intensity [11,24–27].
In contrast, the sLT is observed simultaneously with the bLT [28,29], which manifests as
an increased production of the lactate in response to the intensive energy demand in the
muscles. Such different mechanisms of OS and sweat lactate generation suggest that the
increase in sweat lactate production (indicating sLT) is different from the OS, as shown
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in this study. We assumed that the rate of increase in exercise intensity used in this study
possibly induced a rapid increase in deep body temperature that allowed the OS to occur
earlier than the sLT.
Our previous report showed that there was a signiﬁcant correlation between the
bLT and sLT [7]. Herein, we showed that the sLT was strongly associated with the bLT,
regardless of the OS. Some studies noted that several physiological changes associated with
increased lactate production, such as changes in the autonomic nervous balance, hormones,
acid–base equilibrium, and metabolic dynamics, may explain such a simultaneous increase
in lactate in different bioﬂuids [7,30]. Environmental and biological factors complicate the
interpretation of studies using different subjects because they can induce an earlier OS.
Hyperthermal and humid environments induce sweat production [9], and the sweat rate in
men is signiﬁcantly higher than that in women due to the higher sensitivity of the eccrine
glands to heat and the differences in the hormonal environment [31–34]. The high capacity
for sweating in well-trained athletes supports heat acclimation to maintain high aerobic
tolerance [35–38]. Thus, the effect of an early OS on the validity of the sLT to estimate the
bLT needed to be investigated. As shown in this study, the sLT correlated well with the bLT,
even in cases of early OS, which might lead to the greater validity and wider applicability
of sweat lactate measurements during exercise. This can be attributed to the irrelevancy of
the OS in the sLT due to the difference in the mechanisms of sweating onset and lactate
production. In addition, the OS was poorly correlated with the bLT in the EP and RP
groups, which suggests that the sLT is superior to the OS as a noninvasive parameter to
estimate the bLT.
Insufﬁcient sweating remains a major challenge for the use of lactate sensors. A total
of ﬁve cases showed a later OS than bLT, and the mean difference between the bLT and sLT
(sLT–bLT (s)) was larger than in other cases in the EP and RP groups. This indicates that
the sLT is more likely to be delayed than the bLT. Therefore, the device had difﬁculty in
adapting to the sLT measurement under ambient conditions with poor sweating during
exercise, such as with low-intensity exercise and a low number of sweat glands due to
the genetic background [39]. Adjusting the exercise environment (e.g., humidity and
temperature) and duration (e.g., long warm-up and total exercise time) may be required
for subjects with delayed perspiration. In addition, the use of hermetic sealing or high
local temperatures to promote sweating would overcome insufﬁcient sweating in normal
environments. Otherwise, improved sweat lactate sensing devices may be warranted. A
sweat sampling patch that operates under novel osmotic extraction principles succeeded in
withdrawing sweat without rigorous exertion; however, the data were not continuous [40].
Moreover, further studies are required to examine subjects with less perspiration. In the
ﬁve cases with delayed OS, the sLT strongly correlated with the OS and tended to be more
delayed. Naturally, the sLT was later than the OS because the sLT can be measured only
after the sensor detects sweat. Thus, we assumed that the deviation of the sLT from the bLT
was attributable to the delay in the OS.
Our ﬁndings should be interpreted with the following limitations. First, our results
were validated using a single type of exercise. Generally, different exercise protocols require
different motor functions and associated metabolic pathways. For example, eccentric
exercise leads to lower fatigue and lactate responses than concentric exercise with an
equivalent load [41,42]. The OS may be delayed during exercise with a signiﬁcantly short
warm-up time. Second, because of the observational study design, the inﬂuence of selection
bias cannot be completely excluded. The current study mainly included healthy college-
aged men and had a relatively small number of cases, especially untrained participants.
Further research is warranted, including with untrained subjects and women, because they
may differ in their sweat kinetics, muscle mass, and lactate kinetics. Third, the sweat lactate
values were obtained from the upper arms. Regional differences in sweat kinetics during
cycling exercises have already been reported, and some have revealed higher sweating
rates in the forehead and chest [11,43]. The relationship between the sweat kinetics and
sweat lactate kinetics at different sites should be analyzed. Fourth, other parameters of
Sensors 2023, 23, 3378
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sweat dynamics, such as the local sweat rate, were not examined. The OS is an observed
phenomenon at the same deep body temperature, even at different relative intensities
of exercise. In contrast, the sweat rate increases signiﬁcantly with increasing exercise
intensity [11]. The effects of different local sweat rates on the sLT should also be examined.
5. Conclusions
The results of this study revealed a poor correlation between the OS and sLT, which
supports the notion that the sLT is strongly correlated with the bLT, independent of the
preceding OS during incremental exercise in healthy men. These ﬁndings provide further
information regarding LT measurements of sweat lactate as a noninvasive, continuous,
real-time analytical alternative to blood lactate testing.
Supplementary Materials: The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/s23073378/s1. Figure S1: Sweat lactate sensing device.
Author Contributions: Conceptualization, Y.M., H.O., T.S., D.N. and Y.K. (Yoshinori Katsumata);
methodology, Y.M., H.O. and T.S.; formal analysis, Y.M., H.O., and T.S.; investigation, Y.M., H.O.,
T.S., J.N., H.F., K.I., S.H. and Y.K. (Yusuke Kobari); resources, D.N. and Y.K. (Yoshinori Katsumata);
data curation, Y.M., H.O. and T.S.; writing—original draft preparation, Y.M.; writing—review and
editing, H.O., T.S., D.N., Y.K. (Yoshinori Katsumata), J.N., H.F., K.I., S.H. and Y.K. (Yusuke Kobari);
visualization, Y.M., H.O., T.S. and Y.K. (Yoshinori Katsumata); supervision, M.N. and T.N.; project
administration, D.N. and Y.K. (Yoshinori Katsumata); funding acquisition, D.N. and Y.K. (Yoshinori
Katsumata). All authors have read and agreed to the published version of the manuscript.
Funding: This study was funded by the Japan Agency for Medical Research and Development (award
numbers: 19ek0210130h0001, 20ek0210130h0002, and 21ek0210130h0003) and the Keio University
Global Research Institute IoT Healthcare Research Consortium (grant number: 02-066-0008).
Institutional Review Board Statement: The study was conducted in accordance with the Declaration
of Helsinki and ethical guidelines for medical and health research involving human participants and
was approved by the Institutional Review Board of our institution (approval no. 20180357).
Informed Consent Statement: Each participant provided written consent after being fully informed
of the study purpose, possible risks, or discomfort associated with the experimental protocol, as well
as the publication of the ﬁndings before enrollment. Written informed consent was obtained from the
subjects for the publication of this paper.
Data Availability Statement: The datasets supporting the conclusions of this study are available
from the corresponding author upon reasonable request.
Acknowledgments: We are grateful to Daichi Nishiumi and Yoshikazu Kikuchi for their assistance
with this study.
Conﬂicts of Interest: Daisuke Nakashima is the president of Grace Imaging, Inc., and holds shares
in this company, which sells lactic-acid-sensing equipment. Daisuke Nakashima was not involved in
the data acquisition and analysis.
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Implications of the Onset of Sweating on the Sweat Lactate Threshold.
Maeda, Yuta,Okawara, Hiroki,Sawada, Tomonori,Nakashima, Daisuke,Nagahara, Joji,Fujitsuka, Haruki,Ikeda, Kaito,Hoshino, Sosuke,Kobari, Yusuke,Katsumata, Yoshinori,Nakamura, Masaya,Nagura, Takeo