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Reliability of threshold 
determination using 
portable muscle oxygenation 
monitors during exercise 
testing: a systematic review 
and meta‑analysis
Carlos Sendra‑Pérez 
1, Jose Luis Sanchez‑Jimenez 
1, Joaquín Martín Marzano‑Felisatti 
1, 
Alberto Encarnación‑Martínez 
1,2, Rosario Salvador‑Palmer 
2,3 & Jose I. Priego‑Quesada 
1,2,3*
Over the last few years, portable Near‑Infrared Spectroscopy (NIRS) technology has been suggested 
for determining metabolic/ventilator thresholds. This systematic review and meta‑analysis aimed 
to assess the reliability of a portable muscle oxygenation monitor for determining thresholds during 
exercise testing. The proposed PICO question was: Is the exercise intensity of muscle oxygenation 
thresholds, using portable NIRS, reliable compared with lactate and ventilatory thresholds for 
exercise intensity determined in athletes? A search of Pubmed, Scopus and Web of Science was 
undertaken and the review was conducted following PRISMA guidelines. Fifteen articles were 
included. The domains which presented the highest biases were confounders (93% with moderate 
or high risk) and participant selection (100% with moderate or high risk). The intra‑class correlation 
coefficient between exercise intensity of the first ventilatory or lactate threshold and the first muscle 
oxygenation threshold was 0.53 (obtained with data from only 3 studies), whereas the second 
threshold was 0.80. The present work shows that although a portable muscle oxygenation monitor 
has moderate to good reliability for determining the second ventilatory and lactate thresholds, further 
research is necessary to investigate the mathematical methods of detection, the capacity to detect 
the first threshold, the detection in multiple regions, and the effect of sex, performance level and 
adipose tissue in determining thresholds.
In many sports, various methods of exercise testing are performed for detecting metabolic/ventilatory thresh-
olds. These zones or points are characterized by nonlinear increases of physiological outcomes (e.g., dot(V), 
oxygen volume  (VO2), blood lactate, heart rate, etc.) so determining two physiological breakpoints that allow the 
three-phase model of intensities to be  applied1–3. These data are important to trainers and athletes for assessing 
physical condition and programming intensities to optimize training and improving cardiovascular fitness and 
 endurance4,5. Therefore, it is of great importance to have a reliable method for threshold  detection6.
The ventilatory or metabolic threshold is usually determined by gas exchange or blood lactate data respec-
tively, obtained during incremental  tests4,7. Gas exchange is one of the most commonly used methods for assess-
ing the evolution of gas exchange measurements (dot(V),  VO2, carbon dioxide volume  (VCO2) and minute 
ventilation (VE)) that allow detection of the respiratory compensation point (also referred to as ventilatory 
OPEN
1Research Group in Sports Biomechanics (GIBD), Department of Physical Education and Sports, Faculty of Physical 
Activity and Sport Sciences, Universitat de València, C/Gascó Oliag, 3, 46010 Valencia, Spain. 2Red Española 
de Investigación del Rendimiento Deportivo en Ciclismo y Mujer (REDICYM), Consejo Superior de Deportes 
(CSD), Facultad de Ciencias de la Actividad Física y del Deporte, Campus d’Ontinyent, Laboratorio Biomecánica, 
Avda. Conde de Torrefiel n° 22, 46870 Ontinyent, Spain. 3Biophysics and Medical Physics Group, Department of 
Physiology, Universitat de València, Faculty of Medicine and Odontology, Avd. Blasco Ibañez 15, 46010 Valencia, 
Spain. *email: j.ignacio.priego@uv.es
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threshold (VT))8. For example, one method that is often used is the ventilatory method consists of determining 
the first and second ventilatory thresholds by detecting nonlinear increases in minute ventilation, the ventila-
tory equivalent for oxygen, the ventilatory equivalent for carbon dioxide, oxygen uptake, and carbon dioxide 
 production9. Another widely used method is the blood lactate  measurement10. In contemporary physiology, 
lactate is considered a major metabolic intermediate that has a wide-ranging impact on energy substrate utiliza-
tion, cell signaling, and  adaptation11. It is also important for the mitochondria since lactate is the end product 
of glycolysis and plays a role in connecting oxygen-independent and oxygen-dependent energy production, as 
a major energy source for mitochondrial  respiration4,11. Hence, lactate enters the mitochondrial reticulum to 
support cell energy homeostasis by oxidative phosphorylation, and this process helps lactate  disposal11. Threshold 
determination using blood lactate concentration can be obtained from values fixed (e.g., 2 or 4 mmol  L−1)12 to 
mathematical  models13,14.
However, both methods have associated limitations such as the economic cost of gas exchange, and the neces-
sity to extract a drop of blood or its incapacity to measure continuously for  lactate15, all of which makes it inter-
esting to explore new methodologies. Moreover, it has been suggested that determining thresholds using muscle 
oxygen saturation  (SmO2) could be a valid alternative to pulmonary gas exchange or blood lactate  methods16,17.
Muscle oxygenation based on Near-Infrared Spectroscopy (NIRS) is a non-invasive technology that was 
described for the first time by Jöbsis in 1977, for monitoring in vivo cerebral  oxygenation18. Nowadays, it is 
becoming very popular in the sports training field, thanks to the appearance of more affordable, easy to apply, and 
portable measuring  devices19,20. Currently, NIRS technology is based on the modified Beer-Lambert’s law, which 
considers the dispersion of the nature of the tissues and their  geometry21,22 (Eq. 1). NIRS technology detects the 
oxyhemoglobin  ([O2Hb]) or deoxyhemoglobin ([HHb]) depending on light absorption, but in both cases, hemo-
globin or myoglobin are referenced, since NIRS technology does not differentiate between chromophores (Eq. 2).
Modified Beer-Lambert’s law Eq. (1), where “A” is the absorption, “I” is the luminous intensity (lm  sr−1), 
“ ε ” is the extinction coefficient for the light absorbing compound of interest, “[C]” is the concentration of the 
compound of interest (e.g. [Hb], [Mb] and/or  [cytox]), “L” is the source-detector distance (mm), “DPF” the dif-
ferential path length factor and “G” is the factor reflecting non-absorption.
Equation for calculating muscle oxygen saturation  (SmO2) by the oxyhemoglobin  (O2Hb) and deoxyhemo-
globin (HHb) measured.
NIRS technology in the sports field is being used to observe changes in the muscle metabolism of different 
 muscles19. This has allowed us to measure local muscle performance during exercise, determining whether the 
muscles work optimally and if there is deoxygenation depending on exercise  intensity20,23,24. Moreover, although 
several studies have suggested that portable NIRS technology can be used for determining muscle oxygenation 
 thresholds17,25,26, and many studies have been published over the last few years, as far as the author knows, no 
systematic reviews and meta-analyses that validate the use of NIRS technology to detect thresholds have been 
undertaken.
Therefore, the aim of this systematic review and meta-analysis was to evaluate the reliability of determining 
the exercise intensity of the muscle oxygenation threshold (using the portable NIRS) compared with detection, 
using a gold standard method during laboratory and field tests.
Methods
Literature search methodology. 
This systematic review and meta-analysis was carried out following 
the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)  statement27. The proposed 
PICO (Population, Intervention, Comparison and Outcomes of an article) question was: Is the exercise intensity 
of muscle oxygenation thresholds, using portable NIRS, reliable compared with lactate and ventilatory thresh-
olds for exercise intensity determined in athletes? Three databases (PubMed, Scopus and Web of Science) were 
electronically searched on the 15th of June of 2023 using the following terms: “NIRS” OR “Near Infrared Spec-
troscopy” OR “muscle oxygenation” OR “oximetry” AND with the terms and synonyms “threshold” OR “break-
point” OR “inflection point”. Additionally, (AND) different terms such as “exercise” OR “sport” OR “physical 
activity” OR “running” OR “cycling” OR “swimming” were used. Every database employed its own term map-
ping. The results were screened to identify relevant studies, first by abstract and finally by full text. Full texts 
underwent a thorough screening process to determine their eligibility for inclusion in the review. Only those 
texts that fulfilled all the predetermined criteria were considered for inclusion.
The articles obtained were exported to Zotero (version 6.0.15, Corporation for Digital Scholarship, Vienna, 
USA) to eliminate duplicates, and the abstracts were uploaded to JBI SUMARI (The University of Adelaide, 
Adelaide, Australia) to carry out the first screening.
Inclusion and exclusion criteria. 
The inclusion criteria established for the systematic review were as fol-
lows: (1) Only studies written in English, Spanish or Portuguese, (2) studies using a portable and commercial 
NIRS for muscle oxygenation threshold detection, (3) studies using a gold standard (gas exchange or blood lac-
tate methods) in addition to muscle oxygenation for thresholds detection, (4) studies with a healthy population 
between 18 and 65 years of age, and (5) experimental and quasi-experimental studies.
(1)
A = log I
IO
= ε[C]L ∗ DPF + G
(2)
SmO2 =
O2Hb
O2Hb + HHb × 100
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Study selection and data extraction. 
The first screening was performed by reviewing the abstracts of 
articles, after removing duplicates. Then, the selected articles were fully read to reach a decision. The entire 
process was carried out by two reviewers. When there was a disagreement on an abstract or article, it was sub-
sequently discussed until a consensus was reached. For each study, the extracted data were: the authors and the 
year, the participants, a short description of the protocol, the thresholds calculated, the NIRS brand, the NIRS 
location, and the results. The data from each included article were extracted by two reviewers and confirmed by 
a third. Participants were categorized as elite, highly trained, trained and recreationally active following previous 
 guidelines28,29.
Risk of bias and quality of evidence assessment. 
The quality of the quasi-experimental studies 
included in the systematic review was assessed by two reviewers working independently using the ROBINS-I 
Scale. The ROBINS-I Scale evaluates risk of bias across 7 domains: confounding, selection of participants, clas-
sification of interventions, deviations from intended interventions, missing data, measurement of outcomes and 
the selection of the reported  results30. For each domain, the risk of bias assessment was categorized: no informa-
tion, critical, serious, moderate or  low30. When there was a disagreement between the reviewers a third reviewer 
was consulted.
Meta‑analysis. 
A separate meta-analysis was performed to examine the reliability in determining intensity 
at each threshold using NIRS and the gold standard method (gas exchange and/or blood lactate). The intraclass 
correlation coefficient (ICC) and sample size were extracted for each study. For the studies that did not provide 
ICC values, the ICC value was calculated from obtaining the data from the datasets, tables and figures of the 
article, or on request from the authors. In the case of figures, data was extracted from scatter plots using the plot 
digitizer  application31. If the data were not provided by the authors, the study was excluded from the analysis. 
ICC values were calculated based on a single rater-measurement, absolute-agreement, and 2-way random-effects 
model. For studies where it was possible to obtain more than one ICC value (e.g., because the intensity at the 
threshold was extracted using different automatic methods), these ICC values were averaged, using only one 
ICC value for each study to avoid statistical  dependence31,32. ICC values were transformed to Fisher’s z scale and 
a random-effects model with Restricted Maximum Likelihood Estimation was used for the  analysis33, assessing 
the type of gold standard compared (gas exchange or blood lactate) as a possible moderator. Q and I2 statistics 
were used for the homogeneity analysis. I2 values of around 25%, 50%, and 75% denoted low, moderate, and 
large heterogeneity, respectively. To assess the publication bias, funnel plot with Duval and Tweedie’s trim-and-
fill method for imputing missing data and the Egger’s test were  performed34,35. To facilitate the interpretation 
of the data, Fisher’s z values were then converted back to ICC values after completing the meta-analyses33. The 
ICC and associated 95% confidence intervals were interpreted as: poor (0.00–0.25), fair (0.26–0.50), moderate 
(0.51–0.75) and good (0.76–1.00)36. Statistical significance was established at p < 0.05. A meta-analysis was per-
formed with the “metafor” package (version 4.2-0)37 in RSTUDIO (version 2023.06.0)38.
Results
Study selection. 
A total of 1,131 articles from databases of PubMed (237), Web of Science (507), and Sco-
pus (387) were included, and 559 articles remained after removing duplicates. Finally, after selecting studies by 
their abstracts, 129 full articles were reviewed, of which 15 were included in the systematic review (Fig. 1).
Participants characteristics. 
The systematic review included a sample of 344 participants (216 males and 
128 females). Among these participants 33 were elite athletes, 208 highly trained athletes, 31 trained athletes and 
72 recreationally active athletes. Moreover, athletes from various sports were included (soccer, cycling, running, 
triathlon and rowing) with laboratory protocols, since there are currently no studies carried out in field tests. The 
study characteristics and the main findings are summarized in Table 1.
Methods used for determining muscle oxygenation threshold. 
The studies selected had deter-
mined both muscle oxygenation threshold (MOT) (first and second) using different methods (Table 2). Most of 
the studies used the regression double linear representing 42% and wearable lactate threshold (WLT) was used in 
25% of the studies included in the systematic review. Together, these two methods represented 67% of the studies 
included in the systematic review. However, visual identification was also used in two studies (17%).
Risk of bias evaluation. 
The domains which presented the highest bias were due to confounding (7% with 
critical risk, 33% with serious risk and 53% with moderate risk), due to the selection of the participants (20% 
with serious risk and 80% with moderate risk), and due to the selection of the reported results (40% with moder-
ate risk) (Figs. 2 and 3). For the other domains, most of the studies presented a low risk of bias (> 85%).
Meta‑analyses. 
Of the 15 articles included in this review, the ICCs of 13 of them were obtained from the 
meta-analysis (Table 3). Of these 13 articles, the ICC was provided in the article itself in 3, was calculated from 
the data obtained in a dataset, table or figure in 8, and in 2 the ICC was provided directly by the authors (Table 3).
A test of moderators was not performed for the first threshold due to the low number of studies (n = 3, 
Table 3). The Q test was not significant (Q(df = 2) = 1.01, p-val = 0.60) and the I2 was 0%, showing a low hetero-
geneity. The Trim-and-fill method estimated 0 missing studies and Egger’s test was not significant (p = 0.46). The 
ICC of the first threshold was moderate (ICC = 0.53) but with a wide 95%CI[0.31, 0.69] (Fig. 4A).
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For the second threshold, no effect of moderators was observed (p = 0.94) at first. Therefore, a meta-analysis 
was performed without differentiating between the ICC obtained compared with lactate or gas exchange. The Q 
test was not significant (Q(df = 13) = 99.17, p < 0.001) and the I2 was of 86%, showing a large heterogeneity. The 
Trim-and-fill method estimated 0 missing studies and Egger’s test was not significant (p = 0.54). The ICC of the 
second threshold was good (ICC = 0.80, 95%CI[0.65, 0.89] (Fig. 4B).
Discussion
The aim of this systematic review and meta-analysis was to evaluate the reliability of determining exercise inten-
sity using the muscle oxygenation threshold (with the portable NIRS) compared with a gold standard detection 
method during laboratory tests. The results of the review show that the methods mostly used to determine muscle 
oxygenation thresholds were regression double linear (46%), WLT (20%), and visual identification (20%). The 
meta-analysis revealed that of the 13 studies where ICC was obtained, only 3 studies assessed the first threshold, 
the mean ICC of 0.53 being observed between the exercise intensity obtained at the first muscle oxygenation 
threshold  (MOT1) and first lactate threshold  (LT1) or first ventilatory threshold  (VT1). The mean ICC between 
second muscle oxygenation threshold  (MOT2) and second lactate threshold  (LT2) or second ventilatory threshold 
 (VT2) was 0.80.
Our meta-analyses were focused on showing whether the exercise intensity where the first and second thresh-
olds were detected using the portable NIRS was more reliable than the gold standards methods (gas exchange 
and blood lactate). Table 1 shows how the relationship between MOT and VT was analyzed in 7  studies16,25,39–43 
and in 9 studies for  LT17,26,41,44–49.
Figure 1.  Study selection from the systematic review and meta-analysis (PRISMA).
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Study (year)
Participantsa
Protocol
Thresholds
NIRS device
NIRS location
Results/conclusions
Batterson et al.44
N = 10 (M)
Elite
Soccer players
GXT
3-min work + 30 s rest
9.0 km·h−1 W-UP
↑ 1.8 km·h−1 every 180 s
*Treadmill
LT2,  LT1
Moxy
VL, GC, BF
MOT1 and  MOT2 showed 
similar that  LT1 and  LT2 in all 
muscles analyzed. This show 
that  SmO2 is useful for coaches
Borges and  Driller45
N = 7 (M); 7 (F)
Highly trained athletes
GXT
3-min W-UP (4.8 km·h−1)
3-min (9.3–11.7 km·h−1)
↑ 0.3–1.1 km·h−1 every 180 s
*Treadmill
LT2
BSX Insight
GC
MOT2 showed a high cor-
relation. The wearable lactate 
threshold sensor could be 
implemented by coaches and 
athletes
Cayot et al.46
N = 9 (M); 5 (F)
Recreationally
2 × GXT
(separated by 7–10 days)
5-min W-UP (25 W)
↑ 25 W every 180 s
*Cycle ergometer
LT2
Moxy
VL
MOT2 detection was moderately 
correlated with the  LT2 and the 
heart rate. The results do not 
support the use of two different 
mathematical methods for 
 MOT2 determination
Contreras-Briceño et al.39
N = 8 (M); 7 (F)
Highly trained triathletes
GXT
2-min rest
3-min W-UP (100 W)
↑ 20 W every 80 s
*Bike on a cycle ergometer
VT2
Moxy
7th IC
A good-to-excellent correlation 
was obtained between  MOT2 
and  VT2 for each variable of all 
analyses in the 7th IC, muscle
Driller et al.47
N = 10 (M); 5 (F)
Highly Trained Cyclists
GXT
3-min 80–120 W
↑ 20 W every 180 s
*Bicycle on an ergometer
LT2
BSX Insight
GC
LT2 determination through 
 MOT2 showed an excellent 
correlation during cycling. 
These results were shown in all 
methods for  LT2 detection
Farzam et al.48
N = 15 (M); 3 (F)
Recreationally
GXT
4-min 30 W
↑30 W every 240 s
*Cycle ergometer
LT2
Humon Hex,
MetaOX*
RF
MOT2 determination showed 
good agreements with  LT2
NIRS portable and NIRS 
non-portable showed a good 
correlation during the exercise. 
A low-cost, wireless, wearable 
NIRS is a good predictor of the 
threshold
Feldmann et al.16
N = 6 (M); 4 (F)
Recreationally cyclists and 
runners
GXT
Run test:
5-min W-UP (3.0–3.5 km·h−1)
↑0.5 km·h−1 every 30 s
Cycling test:
5-min W-UP (50–100W)
↑25W every 25 s
*Treadmill
*Cycle ergometer
VT1,  VT2
Moxy
VL
NIRS technology is suitable for 
determining VT1 and VT2. 
Additionally,  SmO2min is a good 
indicator of cardiorespiratory 
fitness, as it correlated with 
 VO2peak. Furthermore, no matter 
in which lateral vastus (right or 
left) the NIRS device was placed 
and the modality (cycling or 
running) it detected the MOT
correctly
McMorries et al.49
N = 7 (M); 14 (F)
Trained
Triathletes
GXT
↑ 12–18 s per km every 180 s
*Treadmill
LT2
BSX Insight
GC
MOT2 showed similar values to 
 LT2, when the thresholds were 
compareted using the heart rate
Raleigh et al.41
N = 31 (M)
Highly trained cyclist/triathletes
GXT
15-min W-UP (120 W)
3-min (100 W)
↑ 25 W every 180 s
*Cycle Ergometer
LT2,  VT2
Moxy
VL
MOT2,  LT2 and  VT2 were not 
different, but a poor correlation 
was obtained between them
A good correlation was identi-
fied between  VT1 and  LT1
Rodrigo-Carranza et al.25
N = 5 (M); 5 (F)
Highly trained runners
GXT
5-min W-Up (9 km·h−1)
↑ 1 km·h−1 every 60 s
*Treadmill
VT2
Humon Hex
VL
VT2 and  MOT2 were positively 
correlated during running. 
Thus, the device presented a 
good predictor of the second 
threshold
Osmani et al.40
N = 16 (M); 5 (F)
Recreationally
GXT
3-min work + 30 s rest
8.0 km  h−1 W-UP
↑ 1.2 km  h−1 every 180 s
*Treadmill
VT2
Humon Hex
VL
SmO2 data alone were not 
enough to determine the  VT2
Also,  SmO2 values of this device 
(Humon) do not correlate with 
other variables (blood lactate, 
RPE, HR and running power)
Salas-Montoro et al.17
N = 32 (M); 58 (F)
23 Elite
67 Highly trained
Cyclists
GXT
5-min W-Up
(15–20% of FTP)
↑ 25 W every 60 s
*Cycle ergometer
LT2
Humon Hex
RF
LT2 was excellently correlated 
with  MOT2 when compared 
using power output, percentage 
of maximal aerobic power, heart 
rate and percentage of maxi-
mum heart rate to MOT. The 
reliability of methods showed 
very good or excellent values in 
all cases (0.74–0.99)
NIRS portable device can be an 
interesting tool for threshold 
detection for coaches without 
performing an on-site lactate 
test
Continued
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The studies of Feldmann et al.16 and Van der Zwaard et al.42 compared the  VT1 and  LT1 with  MOT1 in cycling 
and found ICC values (ICC = 0.56–0.65). These results are in line with other studies that determined thresh-
olds with non-portable NIRS in  cycling50. Moreover, a fair ICC in running was shown (ICC = 0.23–0.49)16,44. 
28/07/2023 17:06:00 A lower number of studies assessed the first threshold compared with the second one (3 vs. 
12 studies), maybe due to the difficulty of determining the  MOT1, since the slope changes very slightly and the 
ICC value is not as good as the second  threshold42.
The second threshold was determined using the blood lactate concentration and muscle oxygenation in 
different sports such as  cycling16,17,46–48,  running44,45,49 and  rowing26. ICC values showed a certain disparity and 
were fair, moderate or good (ICC = 0.29–0.90) in studies of running, although cycling studies showed a good 
ICC (ICC = 0.91–0.94). However, the ICC value of two studies were not  obtained46,48. The remaining studies also 
Table 1.  Summary of selected studies. W: watts; M: male; F: female; GXT: graded exercise test; W-UP: warm 
up; R: recovery;  LT1: first lactate threshold;  LT2: second lactate threshold;  VT1: first ventilatory threshold;  VT2: 
second ventilatory threshold; BF: biceps femoris; LD: lateral deltoid; IC: intercostal; RF: rectus femoris; VL: 
vastus lateralis; GC: gastrocnemius; RCP: respiratory compensation point;  MOT1: first muscle oxygenation 
threshold;  MOT2: second muscle oxygenation threshold. a Data are expressed as mean ± standard deviation. 
*Non-portable NIRS.
Study (year)
Participantsa
Protocol
Thresholds
NIRS device
NIRS location
Results/conclusions
Turnes et al.26
N = 13 (M)
Highly trained rowers
(1) GXT
3-min (130 W)
↑30 W every 180 s//R 30″
(2) 10-min W-Up + 5-rest
2000 m test
*Rowing ergometer
LT2
Portamon
VL
LT2 was moderately related to 
 MOT2 during the rowing incre-
mental test. However, the  SmO2 
in the VL presented a large vari-
ability between participants
Van der Zwaard et al.42
N = 30 (M); 10 (F)
9 Recreationally
10 Trained
21 Highly trained
Cyclist and endurance trained
GXT
3-min 1.5 W·kg−1 (85–145 W)
↑ 0.5 W·kg−1 (30–50 W) every 
180 s
*Cycle ergometer
VT1,  VT2
Portamon
VL
VT1 and  VT2 were moderately 
related to MOT. The relation-
ship increased in trained cyclists 
(0.68–0.84) compared with 
recreationally trained males 
(0.48–0.50)
VT differed across sexes and 
training status, whereas MOT 
differed only across sexes
Yogev et al.43
N = 17 (M); 5 (F)
Highly trained
Cyclist
GXT
6-min W-Up (110–140 W)
4-min (70–100 W)
↑1 W every 2 s
*Stationary bicycle trainer
VT2
Moxy
LD, VL
VT2 and  MOT2 showed a 
moderate relationship in both 
muscles
The athletes and trainers could 
use portable NIRS to detect 
MOT
Table 2.  Methods for determining the muscle oxygenation threshold in the studies selected. MOT1: first 
muscle oxygenation threshold,  MOT2: second muscle oxygenation threshold. a Also visually checked. 
b Inflection point at  SmO2 values at the same point as the  VT2.
Methods for determining the MOT
N
(%)
Threshold
Studies
Regression double linear
7
46
MOT1 &  MOT2
16,26,39,41–44a
Wearable lactate threshold (WLT)
3
20
MOT2
45,47,49
Visual identification (decrease of more than 15%)
3
20
MOT2
17,25,40b
Application Humon Beta
2
7
MOT2
48
D-max or modified D-max
1
7
MOT2
46
Figure 2.  Risk of bias summary. Created with ‘robvis’  application54.
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compared gas exchange with muscle oxygenation in the second threshold in  cycling16,39,42,43 and  running16,40. The 
results of the different studies suggest that the relationship between both methods in threshold determination is 
affected by the region assessed by the NIRS device, as good values (ICC = 0.92–0.97) were observed on assessing 
the intercostalis during  cycling39. Moreover, the vastus lateralis presented moderate or good ICC in different 
 investigations25,42, so the test or determination method chosen may also be critical.
Different methods were developed to determine the thresholds in blood lactate concentration and gas 
exchange, which are commonly combined by users to find the most optimal inflection  point51. Despite recent 
research into the application of NIRS technology for the purpose of obtaining thresholds, there is a lack of 
research on its methods of determination. The articles included in this systematic review use different methods 
for determining thresholds: BSX Insight (20%, N = 3)45,47,49, double linear regression (46%, N = 7)16,26,39,41–44, visual 
 method17,25,40, Dmax or modified  Dmax46 and applications of devices Humon  Beta48.
BSX Insight, which determines the threshold by making a comparison with blood lactate concentration, 
presented good values of ICC, although this used a patented method to determine MOT based on the inflection 
point of  SmO2 during incremental  testing45. However, as this system is commercial and patented, specific details 
of the algorithm used for said detection are unknown. Another important method is visual, which could be the 
most accurate for detecting the  thresholds17 but with associated human error, or complementary to the previous 
one as was performed by Turnes et al.26 We recommend that future studies explore different methods to analyze 
thresholds using NIRS technology, to provide evidence on which are optimal, if several should be combined, or 
if some are more suitable for certain populations or sports.
Figure 3.  The risk of bias for each study. Created with ‘robvis’  application54.
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The muscles analyzed with NIRS portable had previously been studied by Perrey &  Ferrari19, who showed 
that  SmO2 was determined among different muscles (vastus lateralis, gastrocnemius medialis, intercostal, triceps 
brachii) and many sports (swimming, strength, skiing, speed skating, sailing, running, rugby, climbing, handball, 
cycling, kayak, judo, rowing, football, alpine skiing). Vastus lateralis was the muscle most  assessed16,25,26,40–44,46, 
although other muscles such as  gastrocnemius44,45,47,49, rectus  femoris17, biceps  femoris44, lateral  deltoid43 or 
 intercostal39 were also evaluated. Moreover, the muscles analyzed in each study depend on the sports performed 
in the testing, the main muscles involved in that activity being selected. For example, in cycling the muscle most 
assessed was the vastus lateralis as it is the main muscle contributing to power output production. However, 
some studies explored other regions during cycling which could affect the determination of the  threshold17,47, 
although the rectus femoris is also a power output producer in this area where there could be a higher proportion 
of adipose  tissue52 or because its neuromuscular activation is not affected by the increase in workload during 
the test (e.g., gastrocnemius)53.
The systematic review also focused on exercise testing to determine whether the thresholds in the mus-
cles (local thresholds) were analyzed or whether they are major exercise muscle. The articles included in this 
Table 3.  The intraclass correlations (ICC) for the exercise intensity of muscle oxygenation threshold and 
the gold standard. ICC values used for the meta-analysis are in bold letters. MOT1: first muscle oxygenation 
threshold;  MOT2: second muscle oxygenation threshold;  LT1: first lactate threshold;  LT2: second lactate 
threshold;  VT1: first ventilatory threshold;  VT2: second ventilatory threshold; mDmax: modified Dmax; VL: 
vastus lateralis; LD: lateral deltoid. Values used for the meta-analyses are in bold/italic.
Study
MOT method
Gold standard method
ICC source
ICC
Batterson et al.44
Segmented linear regression model
LT1 and  LT2 was determined using a 
mDmax
Provided by the authors
LT1 right VL: 0.38
LT1 left VL: 0.08
LT1 ICCmean: 0.23
LT2 right VL: 0.54
LT2 right VL: 0.60
LT2 ICCmean: 0.57
Borges and  Driller45
Wearable lactate threshold sensor (WLT)
LT2 was determined using the follow-
ing methods: LSF, Dmax, mDmax, 
4 mmol·L−1 and an increase greater than 
1 mmol·L−1
Article
LSF: 0.91
Dmax: 0.8
mDmax: 0.89
4mmoL: 0.98
1mmoL: 0.92
ICCmean: 0.90
Contreras-Briceño et al.39
Segmented linear regression model
VT2 was determined with the visual 
method by two blinded researchers
Calculated from data obtained from the 
Fig. 4 of the article
ICC: 0.97
Cayot et al.46
Dmax and modified Dmax
LT2 was determined using a Dmax and 
mDmax
Authors did not provide the dataset after 
requestion
–
Driller et al.47
Wearable lactate threshold sensor (WLT)
LT2 was determined using: TradLT, Dmax, 
mDmax and OBLA
Calculated from data obtained from the 
Fig. 2 of the article
TradLT: 0.96
Dmax: 0.88
mDmax: 0.97
OBLA: 0.96
ICCmean: 0.94
Farzam et al.48
Application Humon Beta
LT2 was determined using the value of 
4 mmol·L−1 lactate
Authors did not provide the dataset after 
requestion
–
Feldmann et al.16
Segmented linear regression model
VT1 and  VT2 were detected with a seg-
mented regression analysis
Provided by the authors
LT1 running: 0.49
LT1 cycling: 0.65
ICCmean: 0.57
LT2 running: 0.92
LT2 cycling: 0.92
ICCmean: 0.92
McMorries et al.49
Wearable lactate threshold sensor (WLT)
LT2 was determined using the value of 
4 mmol·L−1 lactate and an increase greater 
than 1 mmol·L−1
Calculated from data obtained from the 
Figure 6 of the article
ICC: 0.29
Osmani et al.40
Visual identification
VT2 was determined observing an inflec-
tion point
Calculated from data obtained from the  
Tables 1 and 2 of the article
ICC: 0.23
Raleigh et al.41
Segmented linear regression model
VT2 and  LT2 were detected with a seg-
mented regression analysis. The intersec-
tion of two linear segments was defined as 
the threshold
Article
LT2: 0.54
VT2: 0.36
Rodrigo-Carranza et al.25
Visual identification
VT2 was identified by the nonlinear 
increase
Calculated from data obtained from the 
Table 1 of the article
ICC: 0.84
Salas-Montoro et al.17
Visual identification
LT2 was determined in an increase of at 
least 2 mmol·L−1 above baseline measure-
ments
Article
ICC: 0.91
Turnes et al.26
Regression double linear and a visual 
identification
LT was determined by linear interpola-
tion given a fixed concentration of 
3.5 mmol·L−1
Calculated from data obtained from the 
Table 2 of the article
ICC: 0.65
Yogev et al.43
Regression double linear
Regression double linear was used to 
detect the threshold with WKO5. This is 
similar to the V-slope method
Calculated from dataset provided by the 
authors
VT2 VL: 0.73
VT2 LD: 0.79
ICCmean: 0.76
Van der Zwaard et al.42
Intercept of two congregating regression 
lines
VT detection method was the same as in 
MOT detection
Calculated from the dataset (supporting 
files) of the study
ICC VT1: 0.56
ICC VT2: 0.38
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systematic review analyzed 1 or 3 muscles at most at the same time. Moreover, most of these studies were focused 
on correlating the main muscles of exercise with blood lactate concentration or gas exchange, and it is impor-
tant to take into account that lactate and gas exchange determine systemic changes, while NIRS technology can 
be used for determining a more local response. For this reason, further studies that analyze different muscles 
simultaneously would be interesting in order to understand what is happening in each muscle during exercise 
testing, and how some may be more related to systemic changes while others have more specific alterations.
It is important to consider that the present meta-analysis is limited to only one measure of reliability (ICC), 
and more statistics are desirable (e.g., bias between methods) to improve the interpretation and application of 
the present results. Bias was not included due to the low number of studies that reported this data, and the dif-
ferent units used (W, km·h−1or percentage) also posed a challenge. This point should be regarded as a limitation 
of the present work, and future meta-analysis with a higher number of studies should incorporate more reliable 
statistics. Some of the articles included in this review demonstrate mean bias between  MOT2 and  LT2 or  VT2 
ranging from 0.01 and 0.4 km·h−125,44,45,49, between 3.9 and 15.4  W39,41, 0.05 W·kg−117 and 10.7% of the power 
 output26. However, Batterson et al.44 showed a higher mean bias for MOT and  LT1 (1.1–1.2 km·h−1), and Driller 
et al.47 also demonstrated how the method of determination could affect the bias, with the lowest being for the 
Dmax method (17 W) and the highest for the OBLA method (37 W). Finally, the study of Feldmann et al.16 stated 
that in terms of power or speed, the bias represents one performance step (for this particular study, it was 25 W 
for cycling and 0.5 km·h−1 for running).
Although the studies included present low risk of bias in most of the domains assessed, the analysis performed 
suggests that two domains presented a considerable risk of bias: confounders and the selection of the partici-
pants. The main issues related to the confounding domain were the studies that did not consider the effect of 
Figure 4.  Forest plots of the meta-analysis was performed for the intraclass correlation (ICC) of the exercise 
intensity obtained at the first (A) and second (B) threshold determination using NIRS and the gold standard 
(gas exchange or blood lactate).
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the training level of participants, prior activity or sex in their results. In some cases, only the value of correlation 
or intraclass correlation coefficient without the confidence interval appear in the reported results. However, the 
majority of studies had a missing data count bias and bias in measurement outcomes. Future studies should 
take into account these aspects, so as to control them as much as possible, to improve their quality and reduce 
their biases. Moreover, these aspects are possible sources of the high heterogeneity found in the meta-analysis.
The main limitation of the present work is the small number of studies included in the meta-analysis 
(N = 13). In future, a higher number of studies incorporated into the current analysis could corroborate the 
results obtained. Moreover, there was a high heterogeneity between the different studies included. Regarding 
the methodology, the regions or the sample assessed, with participants ranging from national and international 
level  competitors17 to recreational  ones42, could affect the results of the metanalysis.
Considering all the analyses carried out, we think that the following lines of research should be prioritized in 
this area: exploring which are the most appropriate mathematical detection methods depending on the sports or 
populations for NIRS, investigating whether it is possible to detect the first threshold, analyzing multiple regions 
at the same time to find out which ones are most related to systemic thresholds and which have a more specific 
behavior of the muscle itself, and understanding the differences in the detection of thresholds depending on sex, 
performance level, amount of adipose tissue or the changing of muscle length during exercise.
Conclusion
The present systematic review and meta-analysis shows that, although using a portable muscle oxygenation 
monitor has moderate to good reliability for determining the second threshold, further research is necessary to 
investigate the mathematical methods of detection, the capacity to detect the first threshold, detection in multiple 
regions, and the effect of sex, performance level and adipose tissue on threshold determination.
Data availability
The datasets used and/or analysed during the current study available from the corresponding author on reason-
able request.
Received: 5 May 2023; Accepted: 28 July 2023
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Acknowledgements
JM-F contribution was funded by a PhD fellowship (ref. FPU20/01060) from the Ministry of Universities of Spain.
Author contributions
C.S.P. and J.I.P.Q. had the conceptualization of the idea. All the authors contributed to the design of the study. 
C.S., J.S.J. and J.M.F. worked in the data curation. C.S.P. and J.I.P.Q. performed the statistical analysis and the data 
visualization. R.S.P., A.E.M. and J.I.P.Q. supervised the project. C.S.P. wrote the original draft of the manuscript, 
and all authors reviewed, edited, and agreed to the final version of the manuscript.
Competing interests 
The authors declare no competing interests.
Additional information
Correspondence and requests for materials should be addressed to J.I.P.-Q.
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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© The Author(s) 2023

Reliability of threshold determination using portable muscle oxygenation monitors during exercise testing: a systematic review and meta-analysis.
Sendra-Pérez, Carlos,Sanchez-Jimenez, Jose Luis,Marzano-Felisatti, Joaquín Martín,Encarnación-Martínez, Alberto,Salvador-Palmer, Rosario,Priego-Quesada, Jose I