CoChat
Sign Up
Cited by 8Cardiovascular and exercise physiologySports Performance and TrainingMuscle metabolism and nutrition

The DOI matches its Crossref record; the references it cites are not checked yet.

Agreement between Ventilatory Thresholds and Bilaterally Measured Vastus Lateralis Muscle Oxygen Saturation Breakpoints in Trained Cyclists: Effects of Age and Performance

Open access
Reinpõld, Karmen, Rannama, Indrek, Port, Kristjan·2024·Sports
DOI 10.3390/sports12020040PMC PMC10892087
Open this paper in CoChat — free accountOpen PDF

Abstract

This study focused on comparing metabolic thresholds derived from local muscle oxygen saturation (SmO2) signals, obtained using near-infrared spectroscopy (NIRS), with global pulmonary ventilation rates measured at the mouth. It was conducted among various Age Groups within a well-trained cyclist population. Additionally, the study examined how cycling performance characteristics impact the discrepancies between ventilatory thresholds (VTs) and SmO2 breakpoints (BPs). Methods: Junior (n = 18) and Senior (n = 15) cyclists underwent incremental cycling tests to assess their aerobic performance and to determine aerobic (AeT) and anaerobic (AnT) threshold characteristics through pulmonary gas exchange and changes in linearity of the vastus lateralis (VL) muscle SmO2 signals. We compared the relative power (Pkg) at ventilatory thresholds (VTs) and breakpoints (BPs) for the nondominant (ND), dominant (DO), and bilaterally averaged (Avr) SmO2 during the agreement analysis. Additionally, a 30 s sprint test was performed to estimate anaerobic performance capabilities and to assess the cyclists’ phenotype, defined as the ratio of P@VT2 to the highest 5 s sprint power. Results: The Pkg@BP for Avr SmO2 had higher agreement with VT values than ND and DO. Avr SmO2 Pkg@BP1 was lower (p < 0.05) than Pkg@VT1 (mean bias: 0.12 ± 0.29 W/kg; Limits of Agreement (LOA): −0.45 to 0.68 W/kg; R2 = 0.72) and mainly among Seniors (0.21 ± 0.22 W/kg; LOA: −0.22 to 0.63 W/kg); there was no difference (p > 0.05) between Avr Pkg@BP2 and Pkg@VT2 (0.03 ± 0.22 W/kg; LOA: −0.40 to 0.45 W/kg; R2 = 0.86). The bias between two methods correlated significantly with the phenotype (r = −0.385 and r = −0.515 for AeT and AnT, respectively). Conclusions: Two breakpoints can be defined in the NIRS-captured SmO2 signal of VL, but the agreement between the two methods at the individual level was too low for interchangeable usage of those methods in the practical training process. Older cyclists generally exhibited earlier thresholds in muscle oxygenation signals compared to systemic responses, unlike younger cyclists who showed greater variability and no significant differences in this regard in bias values between the two threshold evaluation methods with no significant difference between methods. More sprinter-type cyclists tended to have systemic VT thresholds earlier than local NIRS-derived thresholds than athletes with relatively higher aerobic abilities.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/sports12020040

Cites

Statistics

Figures

Figure 1 Four different types of signal slope change combinations and breakpoints, where plot (C) represents typical sloping pattern and (A,B,D) are atypical; the scales of axes are normalised for individual ranges in SmO2 (nSmO2) detected during the test (Min SmO2 to Max SmO2) and for incremental test duration (start of the first step to the end of exercise) to time (nTime) scale. First breakpoint (BP1) and second breakpoint (BP2).
Figure 1 Four different types of signal slope change combinations and breakpoints, where plot (C) represents typical sloping pattern and (A,B,D) are atypical; the scales of axes are normalised for individual ranges in SmO2 (nSmO2) detected during the test (Min SmO2 to Max SmO2) and for incremental test duration (start of the first step to the end of exercise) to time (nTime) scale. First breakpoint (BP1) and second breakpoint (BP2).CC BY · PubMed Central
Figure 2 Regression and Bland Altman plots were used to illustrate the relationships (A) and correspondence (B) between the timing of SmO2 signal breakpoints (BPs) for typical (types C and D for BP1; A and C for BP2) and atypical (types A and B for BP1; B and D for BP2) slope changes. These plots were particularly focused on instances where opposing directions of slope changes were observed in the contralateral VL muscles, with the difference being calculated as typical minus atypical.
Figure 2 Regression and Bland Altman plots were used to illustrate the relationships (A) and correspondence (B) between the timing of SmO2 signal breakpoints (BPs) for typical (types C and D for BP1; A and C for BP2) and atypical (types A and B for BP1; B and D for BP2) slope changes. These plots were particularly focused on instances where opposing directions of slope changes were observed in the contralateral VL muscles, with the difference being calculated as typical minus atypical.CC BY · PubMed Central
Figure 3 Bland Altman and regression plots between relative power (W/kg) values of VT1 and originally measured SmO2 BP1 of ND side (section A) and DO side (B); average value of ND and DO side (C) and relationship between power values of ND- and DO-side SmO2 BP1 (D).
Figure 3 Bland Altman and regression plots between relative power (W/kg) values of VT1 and originally measured SmO2 BP1 of ND side (section A) and DO side (B); average value of ND and DO side (C) and relationship between power values of ND- and DO-side SmO2 BP1 (D).CC BY · PubMed Central
Figure 4 Bland Altman and regression plots between power values of VT2 and SmO2 BP2 of ND side (A) and DO side (B); average value of ND and DO side (C) and relationship between power values of ND- and DO-side SmO2 BP2 (D).
Figure 4 Bland Altman and regression plots between power values of VT2 and SmO2 BP2 of ND side (A) and DO side (B); average value of ND and DO side (C) and relationship between power values of ND- and DO-side SmO2 BP2 (D).CC BY · PubMed Central
Figure 5 Regression plots representing associations between selected cycling performance characteristics (plot (A) relative VO2@VT2 and plot (B) cyclists’ typology) and bias value between relative power at VT2 and BP2 of SmO2 (average of ND and DO leg).
Figure 5 Regression plots representing associations between selected cycling performance characteristics (plot (A) relative VO2@VT2 and plot (B) cyclists’ typology) and bias value between relative power at VT2 and BP2 of SmO2 (average of ND and DO leg).CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

The citation lists are queued

They are fetched later and appear here then.

References 67 · Cited by 8

Research works printed but not linked: 52

  1. Seiler, 2010, What is best practice for training intensity and duration distribution in endurance athletes?, Int. J. Sports Physiol. Perform., 5, 276, 10.1123/ijspp.5.3.276 · DOI
  2. West, 2004, A century of pulmonary gas exchange, Am. J. Respir. Crit. Care Med., 169, 897, 10.1164/rccm.200312-1781OE · DOI
  3. Perrey, 2022, Muscle oxygenation unlocks the secrets of physiological responses to exercise: Time to exploit it in the training monitoring, Front. Sports Act. Living, 4, 864825, 10.3389/fspor.2022.864825 · DOI
  4. Brocherie, 2021, Under the hood: Skeletal muscle determinants of endurance performance, Front. Sports Act. Living, 3, 719434, 10.3389/fspor.2021.719434 · DOI
  5. Bassett, 2000, Limiting factors for maximum oxygen uptake and determinants of endurance performance, Med. Sci. Sports Exerc., 32, 70, 10.1097/00005768-200001000-00012 · DOI · Paper page
  6. Amann, 2011, Central and peripheral fatigue: Interaction during cycling exercise in humans, Med. Sci. Sports Exerc., 43, 2039, 10.1249/MSS.0b013e31821f59ab · DOI
  7. Feldner, 2019, “Look, your muscles are firing!”: A qualitative study of clinician perspectives on the use of surface electromyography in neurorehabilitation, Arch. Phys. Med. Rehabil., 100, 663, 10.1016/j.apmr.2018.09.120 · DOI
  8. Barstow, 2019, Understanding near infrared spectroscopy and its application to skeletal muscle research, J. Appl. Physiol., 126, 1360, 10.1152/japplphysiol.00166.2018 · DOI · Paper page
  9. Crum, 2017, Validity and reliability of the Moxy oxygen monitor during incremental cycling exercise, Eur. J. Sport. Sci., 17, 1037, 10.1080/17461391.2017.1330899 · DOI · Paper page
  10. Murias, 2013, Systemic and vastus lateralis muscle blood flow and O2 extraction during ramp incremental cycle exercise, Am. J. Physiol. Regul. Integr. Comp. Physiol., 304, R720, 10.1152/ajpregu.00016.2013 · DOI · Paper page
  11. Iannetta, 2017, The near-infrared spectroscopy-derived deoxygenated haemoglobin breaking-point is a repeatable measure that demarcates exercise intensity domains, J. Sci. Med. Sport, 20, 873, 10.1016/j.jsams.2017.01.237 · DOI · Paper page
  12. Keir, 2015, Exercise intensity thresholds: Identifying the boundaries of sustainable performance, Med. Sci. Sports Exerc., 47, 1932, 10.1249/MSS.0000000000000613 · DOI · Paper page
  13. Spencer, 2011, Are the parameters of VO2, heart rate and muscle deoxygenation kinetics affected by serial moderate-intensity exercise transitions in a single day?, Eur. J. Appl. Physiol., 111, 591, 10.1007/s00421-010-1653-x · DOI
  14. Alejo, 2022, Comparative analysis of endurance, strength and body composition indicators in professional, under-23 and junior cyclists, Front. Physiol., 13, 945552, 10.3389/fphys.2022.945552 · DOI
  15. Meredith, 1989, Peripheral effects of endurance training in young and old subjects, J. Appl. Physiol., 66, 2844, 10.1152/jappl.1989.66.6.2844 · DOI
  16. Letnes, 2020, Age-related change in peak oxygen uptake and change of cardiovascular risk factors. The HUNT Study, Prog. Cardiovasc. Dis., 63, 730, 10.1016/j.pcad.2020.09.002 · DOI
  17. Grassi, B., and Quaresima, V. (2016). Near-infrared spectroscopy and skeletal muscle oxidative function in vivo in health and disease: A review from an exercise physiology perspective. J. Biomed. Opt., 21. · DOI · Paper page
  18. Bottinelli, 2000, Human skeletal muscle fibres: Molecular and functional diversity, Prog. Biophys. Mol. Biol., 73, 195, 10.1016/S0079-6107(00)00006-7 · DOI
  19. Padilla, 1999, Level ground and uphill cycling ability in professional road cycling, Med. Sci. Sports Exerc., 31, 878, 10.1097/00005768-199906000-00017 · DOI
  20. Koga, 2007, Spatial heterogeneity of quadriceps muscle deoxygenation kinetics during cycle exercise, J. Appl. Physiol., 103, 2049, 10.1152/japplphysiol.00627.2007 · DOI
  21. Saitoh, 2009, Effects of prior heavy exercise on heterogeneity of muscle deoxygenation kinetics during subsequent heavy exercise, Am. J. Physiol. Regul. Integr. Comp. Physiol., 297, R615, 10.1152/ajpregu.00048.2009 · DOI
  22. Carpes, 2010, On the bilateral asymmetry during running and cycling—A review considering leg preference, Phys. Ther. Sport, 11, 136, 10.1016/j.ptsp.2010.06.005 · DOI
  23. Smak, 1999, The influence of pedaling rate on bilateral asymmetry in cycling, J. Biomech., 32, 899, 10.1016/S0021-9290(99)00090-1 · DOI
  24. Reinpõld, K., and Rannama, I. (2023). Oxygen Uptake and Bilaterally Measured Vastus Lateralis Muscle Oxygen Desaturation Kinetics in Well-Trained Endurance Cyclists. J. Funct. Morphol. Kinesiol., 8. · DOI
  25. Roelands, 2013, Guidelines to classify subject groups in sport-science research, Int. J. Sports Physiol. Perform., 8, 111, 10.1123/ijspp.8.2.111 · DOI · Paper page
  26. Geraskin, D., Boeth, H., and Kohl-Bareis, M. (2009). Optical measurement of adipose tissue thickness and comparison with ultrasound, magnetic resonance imging, and callipers. J. Biomed. Opt., 14. · DOI
  27. Rodger, 2016, Evaluation of the Cyclus cycle ergometer and the Stages power meter for measurement of power output in cycling, J. Sci. Cycl., 5, 16
  28. Wasserman, 2005, Principles of exercise testing and interpretation: Including pathophysiology and clinical applications, Med. Sci. Sports Exerc., 37, 1249
  29. Muggeo, 2008, Segmented: An R package to fit regression models with broken-line relationships, R News, 8, 20
  30. Hopkins, 2009, Progressive statistics for studies in sports medicine and exercise science, Med. Sci. Sports Exerc., 41, 3, 10.1249/MSS.0b013e31818cb278 · DOI · Paper page
  31. Ducrocq, 2020, Inorganic phosphate and lactate potentiate the pressor response to acidic stimuli in rats, Exp. Physiol., 105, 613, 10.1113/EP088349 · DOI
  32. Grotle, 2020, Recent advances in exercise pressor reflex function in health and disease, Auton. Neurosci., 228, 102698, 10.1016/j.autneu.2020.102698 · DOI
  33. Faude, 2009, Lactate threshold concepts: How valid are they?, Sports Med., 39, 469, 10.2165/00007256-200939060-00003 · DOI · Paper page
  34. Radegran, 1997, Ultrasound Doppler estimates of femoral artery blood flow during dynamic knee extensor exercise in humans, J. Appl. Physiol., 83, 1383, 10.1152/jappl.1997.83.4.1383 · DOI
  35. Zoladz, 2008, Progressive recruitment of muscle fibers is not necessary for the slow component of VO2 kinetics, J. Appl. Physiol., 105, 575, 10.1152/japplphysiol.01129.2007 · DOI
  36. Poole, 2021, The anaerobic threshold: 50+ years of controversy, J. Physiol., 599, 737, 10.1113/JP279963 · DOI · Paper page
  37. Jones, 2011, Slow component of VO2 kinetics: Mechanistic bases and practical applications, Med. Sci. Sports Exerc., 43, 2046, 10.1249/MSS.0b013e31821fcfc1 · DOI · Paper page
  38. Broker, 1994, Mechanical energy management in cycling: Source relations and energy expenditure, Med. Sci. Sports Exerc., 26, 64, 10.1249/00005768-199401000-00012 · DOI
  39. So, 2005, Muscle recruitment pattern in cycling: A review, Phys. Ther. Sport, 6, 89, 10.1016/j.ptsp.2005.02.004 · DOI
  40. Baum, 2003, Lower extremity muscle activities during cycling are influenced by load and frequency, J. Electromyogr. Kinesiol., 13, 181, 10.1016/S1050-6411(02)00110-4 · DOI
  41. Elmer, S., Barratt, P., Korff, T., and Martin, J. (2010, January 19–23). Joint-specific power production during submaximal and maximal cycling. Proceedings of the ISBS-Conference Proceedings Archive, Marquette, MI, USA.
  42. Turpin, 2017, Can muscle coordination explain the advantage of using the standing position during intense cycling?, J. Sci. Med. Sport, 20, 611, 10.1016/j.jsams.2016.10.019 · DOI
  43. Hug, 2004, Heterogeneity of muscle recruitment pattern during pedaling in professional road cyclists: A magnetic resonance imaging and electromyography study, Eur. J. Appl. Physiol., 92, 334, 10.1007/s00421-004-1096-3 · DOI
  44. Hug, 2008, Interindividual variability of electromyographic patterns and pedal force profiles in trained cyclists, Eur. J. Appl. Physiol., 104, 667, 10.1007/s00421-008-0810-y · DOI
  45. Hug, 2010, Is interindividual variability of EMG patterns in trained cyclists related to different muscle synergies?, J. Appl. Physiol., 108, 1727, 10.1152/japplphysiol.01305.2009 · DOI
  46. Chapman, 2008, Patterns of leg muscle recruitment vary between novice and highly trained cyclists, J. Electromyogr. Kinesiol., 18, 359, 10.1016/j.jelekin.2005.12.007 · DOI
  47. Priego, 2014, Effects of workload level on muscle recruitment in cycling, Hum. Mov., 15, 45, 10.2478/humo-2014-0001 · DOI
  48. Dingwell, 2008, Changes in muscle activity and kinematics of highly trained cyclists during fatigue, IEEE Trans. Biomed. Eng., 55, 2666, 10.1109/TBME.2008.2001130 · DOI
  49. Hawley, 2001, Adaptations to training in endurance cyclists: Implications for performance, Sports Med., 31, 511, 10.2165/00007256-200131070-00006 · DOI
  50. Coyle, 1991, Physiological and biomechanical factors associated with elite endurance cycling performance, Med. Sci. Sports Exerc., 23, 93, 10.1249/00005768-199101000-00015 · DOI
  51. Mezzani, 2010, Age-related prolongation of phase I of VO2 on-kinetics in healthy humans, Am. J. Physiol.-Regul. Integr. Comp. Physiol., 299, R968, 10.1152/ajpregu.00739.2009 · DOI
  52. Zabala, 2022, Determination of second lactate threshold using near-infrared spectroscopy in elite cyclists, Int. J. Sports Med., 43, 721, 10.1055/a-1738-0252 · DOI · Paper page

Metadata

Authors
Reinpõld, K., Rannama, I., & Port, K.
Affiliations
Tallinn University (EE)
Corresponding author
Reinpõld, Karmen
ORCID
Venue
Sports
Publisher
MDPI AG
Published
2024-01-28
Volume
12
Issue
2
Pages
40
ISSN
2075-4663 (ISSN-L)
DOI
10.3390/sports12020040
PMC
PMC10892087
OpenAlex
W4391309133
Cited by
8
References
67
Citations by year
2026: 3 · 2025: 4 · 2024: 1
FWCI
4.00
Type
journal article
Language
en
Open access
Licence
Locations
  • Sports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Physiology (medical) › Cardiovascular and exercise physiology
  • Health Sciences › Medicine › Orthopedics and Sports Medicine › Sports Performance and Training
  • Health Sciences › Nursing › Nutrition and Dietetics › Muscle metabolism and nutrition
Keywords
ventilatory threshold, vastus lateralis, near-infrared spectroscopy, trained cyclists, aerobic threshold, anaerobic threshold, age differences, cycling performance, sprint power, measurement agreement, incremental cycling test, muscle oxygenation, pulmonary gas exchange, limits of agreement

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Agreement between Ventilatory Thresholds and Bilaterally Measured Vastus Lateralis Muscle Oxygen Saturation Breakpoints in Trained Cyclists: Effects of Age and Performance. https://app.cochat.ai/papers/10.3390/sports12020040
The paper
Reinpõld, K., Rannama, I., & Port, K. (2024). Agreement between Ventilatory Thresholds and Bilaterally Measured Vastus Lateralis Muscle Oxygen Saturation Breakpoints in Trained Cyclists: Effects of Age and Performance. Sports. https://doi.org/10.3390/sports12020040