Article

# Comparison of Selected Lactate Threshold Parameters with Maximal Lactate Steady State in Cycling

Authors:
• German Football Association (DFB)
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## Abstract

The aim of the present investigation was to compare power at “onset of blood lactate accumulation” (OBLA), “individual anaerobic threshold” (IAT) and “+1.5 mmol ∙ l⁻¹ lactate model” with power in maximal lactate steady state (MLSS) in cycling. However, there is a lack of studies concerning the absolute individual differences between different lactate parameters and MLSS. A total of 57 male participants performed several 30-min constant-load tests to determine MLSS by measuring blood lactate concentration (BLC). Depending on BLC, power was increased or decreased by 10 W in the following 30-min test. For detecting power at different threshold parameters, an incremental test was performed that began at 40 W and increased by 40 W every 4 min. Highly significant correlations were found between OBLA and MLSS: r=0.89 (mean difference −7.4 W); IAT and MLSS: r=0.83 (mean difference 12.4W), “+1.5 mmol ∙ l⁻¹ lactate model” and MLSS: r=0.88 (mean difference −37.4W). On average, the parameters of OBLA and IAT approximate MLSS with no significant differences. The “+1.5 mmol ∙ l⁻¹ lactate model” underestimates MLSS significantly. Based on Bland-and-Altman, the comparison of power of all threshold parameters with power in MLSS shows great individual differences despite the high regression coefficients and low mean differences between these methods.

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... Three previous studies have attempted to validate use of an OBLA of 4.0 mmol . L -1 with cycle ergometry [15,53,57]. One study found that it overestimated the MLSS (MD = 49 W) when derived from GXT 1 [53]. ...
... The other study reported poor agreement (bias 7 ± 49 W) when OBLA of 4.0 mmol . L -1 was derived from GXT 4 [57]. The final study observed a poor correlation between an OBLA of 4.0 mmol . ...
... Had we employed a standardized GXT (e.g., 35 W increments), and assuming _ W max stayed constant, the range would have been 9-to 15 min. Applying this to our longer duration GXTs resulted in a homogenous duration (GXT 4 : 32-to 39 min), whereas a standardised approach (e.g., 35 W increments) would have resulted in a range of 27-to 46 min [57]. Thus, individualizing GXT protocol design is a useful approach to ensure homogenous test duration [17]. ...
... Three previous studies have attempted to validate use of an OBLA of 4.0 mmol . L -1 with cycle ergometry [15,53,57]. One study found that it overestimated the MLSS (MD = 49 W) when derived from GXT 1 [53]. ...
... The other study reported poor agreement (bias 7 ± 49 W) when OBLA of 4.0 mmol . L -1 was derived from GXT 4 [57]. The final study observed a poor correlation between an OBLA of 4.0 mmol . ...
... Had we employed a standardized GXT (e.g., 35 W increments), and assuming _ W max stayed constant, the range would have been 9-to 15 min. Applying this to our longer duration GXTs resulted in a homogenous duration (GXT 4 : 32-to 39 min), whereas a standardised approach (e.g., 35 W increments) would have resulted in a range of 27-to 46 min [57]. Thus, individualizing GXT protocol design is a useful approach to ensure homogenous test duration [17]. ...
Article
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Background To determine the validity of the lactate threshold (LT) and maximal oxygen uptake () determined during graded exercise test (GXT) of different durations and using different LT calculations. Trained male cyclists (n = 17) completed five GXTs of varying stage length (1, 3, 4, 7 and 10 min) to establish the LT, and a series of 30-min constant power bouts to establish the maximal lactate steady state (MLSS). was assessed during each GXT and a subsequent verification exhaustive bout (VEB), and 14 different LTs were calculated from four of the GXTs (3, 4, 7 and 10 min)—yielding a total 56 LTs. Agreement was assessed between the highest measured during each GXT () as well as between each LT and MLSS. and LT data were analysed using mean difference (MD) and intraclass correlation (ICC). Results The value from GXT1 was 61.0 ± 5.3 mL.kg-1.min⁻¹ and the peak power 420 ± 55 W (mean ± SD). The power at the MLSS was 264 ± 39 W. from GXT3, 4, 7, 10 underestimated by ~1–5 mL.kg-1.min⁻¹. Many of the traditional LT methods were not valid and a newly developed Modified Dmax method derived from GXT4 provided the most valid estimate of the MLSS (MD = 1.1 W; ICC = 0.96). Conclusion The data highlight how GXT protocol design and data analysis influence the determination of both and LT. It is also apparent that and LT cannot be determined in a single GXT, even with the inclusion of a VEB.
... Determination of MLSS requires the performance of a series of constant-intensity tests during multiple laboratory visits, which in practice is burdensome and may disrupt the athletes' training program. Therefore, attempts are made to determine MLSS indirectly based on AT evaluated during a single GXT until volitional exhaustion [22][23][24][25][26][27][28]. However, the results comparing various concepts of AT with MLSS are conflicting [22][23][24][25][26][27][28]. ...
... Therefore, attempts are made to determine MLSS indirectly based on AT evaluated during a single GXT until volitional exhaustion [22][23][24][25][26][27][28]. However, the results comparing various concepts of AT with MLSS are conflicting [22][23][24][25][26][27][28]. ...
... The D max method only slightly underestimates MLSS (by 1.7 ± 3.9 W) and 95% of differences between measurements by the MLSS and D max method range from − 5.9 to 9.3 W. Likewise, the IAT slightly underestimates MLSS (by 4.3 ± 7.9 W), with limits of agreement between − 11.1 and 19.7 W. It should be noted that in our study, IAT was only evaluated for seven cyclists because of the rapid decline of the post-exercise LA value. This is a commonly reported issue with this method [24,48]. For this reason, despite the favorable MLSS estimation, the IAT method may not be useful in practice for some athletes, especially those highly-trained. ...
Article
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Background The maximal lactate steady state (MLSS) is defined as the highest workload that can be maintained for a longer period of time without continued blood lactate (LA) accumulation. MLSS is one of the physiological indicators of aerobic performance. However, determination of MLSS requires the performance of a series of constant-intensity tests during multiple laboratory visits. Therefore, attempts are made to determine MLSS indirectly by means of anaerobic threshold (AT) evaluated during a single graded exercise test (GXT) until volitional exhaustion. The aim of our study was to verify whether AT determined by maximal deviation (Dmax), modified maximal deviation (ModDmax), baseline LA concentration + 1 mmol/l (+ 1 mmol/l), individual anaerobic threshold (IAT), onset of blood lactate accumulation (OBLA4mmol/l) and V-slope methods based on GXT with 3-min stages provide valid estimates of MLSS in elite cyclists. Methods Twelve elite male cyclists (71.3 ± 3.6 ml/kg/min) completed GXT (the increase by 40 W every 3 min) to establish the AT (by Dmax, ModDmax, + 1 mmol/l, IAT, OBLA4mmol/l and V-slope methods). Next, a series of 30-min constant-load tests to determine MLSS was performed. Agreement between the MLSS and workload (WR) at AT was evaluated using the Bland–Altman method. Results The analysis revealed a very high (rs > 0.90, p < 0.001) correlation between WRMLSS and WRDmax and WRIAT. The other AT methods were highly (rs > 0.70) correlated with MLSS except for OBLA4mmol/l (rs = 0.67). The Bland-Altman analysis revealed the highest agreement with MLSS for the Dmax, IAT and + 1 mmol/l methods. Mean difference between WRMLSS and WRDmax, WRIAT and WR+1mmol/l was 1.7 ± 3.9 W, 4.3 ± 7.9 W and 6.7 ± 17.2 W, respectively. Furthermore, the WRDmax and WRIAT had the lowest limits of agreement with the WRMLSS. The ModDmax and OBLA4mmol/l methods overestimated MLSS by 31.7 ± 18.5 W and 43.3 ± 17.8 W, respectively. The V-slope method underestimated MLSS by 36.2 ± 10.9 W. Conclusions The AT determined by Dmax and IAT methods based on the cycling GXT with 3-min stages provides a high agreement with the MLSS in elite cyclists. Despite the high correlation with MLSS and low mean difference, the AT determined by + 1 mmol/l method may highly overestimate or underestimate MLSS in individual subjects. The individual MLSS cannot be properly estimated by V-slope, ModDmax and OBLA4mmol/l methods.
... Über die Jahre wurde eine Vielzahl verschiedener Laktatschwellenmodelle entwickelt, welche sich in (halb)fixe und individuelle Konzepte unterteilen lassen . Die Frage nach der Konstruktvalidität, also der Genauigkeit eines Schwellenkonzepts das MLSS abzubilden, gilt jedoch für die meisten Konzepte als unzureichend erforscht ) und wird für ausgewählte Schwellenmodelle aufgrund interindividueller Heterogenität durch zwei kürzlich veröffentlichte Arbeiten in Frage gestellt (Grossl, De Lucas, De Souza, & Antonacci Guglielmo, 2012;Hauser, Adam, & Schulz, 2014). Die prädiktive Validität, d. h. ...
... In einer weiteren Arbeit (Hauser et al., 2014) Wettkämpfen überprüften , wurde festgehalten, dass insbesondere für homogene Athletengruppen die Laktatschwellen der maximalen Sauerstoffaufnahme überlegen sind (Coyle et al., 1988;Coyle et al., 1991;Farrell et al., 1979;Yoshida et al., 1987). . Auch lässt sich eine Tendenz erkennen, nach der die Korrelationen für fixe und anaerobe Laktatschwellen höher zu sein scheinen als für aerobe Schwellen . ...
... Heterogenität der Ausdauerleistungsfähigkeit der Probanden, beeinflusst werden ). (Beneke, 2003;Billat et al., 2003;Hauser et al., 2014). Gründe hierfür sind mutmaßlich die Beschränkung der Anzahl von Dauertests, insbesondere in leistungssportlichen Probandenkollektiven, sowie die Anforderungen und Möglichkeiten der Trainingssteuerung in der Sportpraxis. ...
Thesis
Studie 1: Die anaerobe Laktatschwelle versucht, das maximale Laktat-Steady-State (MLSS) aus der Laktatleistungskurve eines Stufentests abzuleiten. Offen ist allerdings wie genau die unterschiedlichen Schwellenkonzepte bei der Bestimmung des MLSS sind. Ziel der Studie war es die Stegmann, Dickhuth, Dmax und 4-mmol-Schwellen auf ihre Genauigkeit im Abschätzen des MLSS zu überprüfen (Konstruktvalidität). 26 Männer (25 ± 4 J, 182 ± 5 cm, 79 ± 8 kg) absolvierten einen fahrradergometrischen Stufentest (ST; Start: 50 oder 100 W, + 50 W / 3 min). Es folgten zur Bestimmung des MLSS (242 ± 41 W) Dauerbelastungen über 30 min. Zur Bestimmung des MLSS durfte die Laktatkonzentration zwischen der 10. und 30. min nicht mehr als 1,0 mmol/l ansteigen. Anhand des MLSS wurden die Probanden in zwei Hälften unterteilt: Ausdauertrainierte (AT, n = 13): 3,7 ± 0,3 W/kg und Nicht-Ausdauertrainierte (NAT, n = 13): 2,6 ± 0,4 W/kg. Mittels Bland-Altman-Plots wurden die mittleren Differenzen (MD) und 95 %-Konfidenzintervalle (LoA) dargestellt. Zusammenhänge zwischen dem MLSS und den Schwellen wurden mittels Pearson-Produkt-Moment-Korrelationen berechnet. Im Mittel zeigen alle Schwellen sehr hohe signifikante Korrelationen zum MLSS (r = 0,91 bis 0,94). Die LoA liegen zwischen 10 % und 20 %. Dabei schätzen die Stegmann- (-4 ± 32 W) und Dmax-Schwelle (3 ± 32 W) unabhängig von der Leistungsfähigkeit das MLSS ähnlich genau ab (Homoskedastizität). Die Genauigkeit der Dickhuth- und 4-mmol-Schwelle ist hingegen von der Leistungsfähigkeit der Probanden abhängig (Heteroskedastizität). Im Mittel überschätzt 4-mmol (18 ± 48 W), wohingegen Dickhuth unterschätzt (-11 ± 38 W). Die individuellen Schwellenkonzepte Stegmann und Dmax schätzen das MLSS im Mittel unabhängig von der Ausdauerleistungsfähigkeit annähernd gleich gut und präzise ab. Die 4-mmol-Schwelle überschätzt es vor allem bei Ausdauertrainierten, wohingegen es die Dickhuth-Schwelle bei Untrainierten unterschätzt. Die individuelle Genauigkeit im Abschätzen des MLSS scheint für die Trainingssteuerung akzeptabel, sollte jedoch in Einzelfällen durch Trainingskontrollen überprüft werden. Studie 2: Das 40-km-Zeitfahren (TT) gilt als eine der wichtigsten Wettkampfdistanzen im Radsport. Unklar ist jedoch, inwieweit die Daten aus den klassischen Leistungstests in der Lage sind die Leistung im TT vorherzusagen. Daher war es das Ziel der zweiten Studie, die externe Validität der Laktatschwellenkonzepte hinsichtlich ihrer Genauigkeit in der Prädiktion der Leistung im TT zu überprüfen. 23 Wettkampfradfahrer (29 ± 8 J, 180 ± 6 cm, 74 ± 8 kg, VO2max 59,4 ± 7,4 ml/min/kg) absolvierten im Abstand von je 2 Stunden TT, einen 30-s-Wingate-Test (WT) und einen ST (100 W + 50 W / 3 min). Aus dem ST wurden die maximale Sauerstoffaufnahme (VO2max) und die oben genannten Schwellen bestimmt. Mittels Bland-Altman-Plots wurden MD ± LoA dargestellt. Zusammenhänge zwischen dem TT und den Schwellen wurden mittels Pearson-Produkt-Moment-Korrelationen berechnet. Signifikante Zusammenhänge zur Leistung im TT wurden für Pmax (r = 0,89), Stegmann (r = 0,83), Dickhuth (r = 0,80), Dmax (r = 0,79) und 4-mmol (r = 0,81) und die VO2max (r = 0,56) gefunden. Alle anderen Parameter zeigten keine signifikanten Zusammenhänge. Die MD lag für alle Schwellenkonzepte über 29 W und die LoA über 17 %. Mit einer MD von 103, aber LoA von 12 % hat die Pmax die geringste Streuung in der Differenz zum TT. Ziel einer Leistungsdiagnostik ist es auch, die aktuelle Wettkampfleistungsfähigkeit eines Sportlers zu ermitteln. Dazu scheint die Pmax für die Leistung im 40-km-Zeitfahren nominell ein besserer Prädiktor zu sein, als die submaximalen Laktatschwellen. Allerdings kann die individuelle Abweichung dennoch erheblich sein. Leistungstests, für die eine maximale Ausbelastung nötig ist, können störende Eingriffe im Trainingsalltag von Leistungssportlern darstellen. Daher wäre es vorteilhaft, einen Parameter der Leistungsdiagnostik zu haben, der unabhängig von Ausbelastung und Ermüdung die Ausdauerleistungsfähigkeit diagnostizieren könnte. Ziel der dritten Studie war es daher, den Einfluss einer Ermüdung auf die vier Laktatschwellen sowie andere Parameter der Leistungsdiagnostik zu überprüfen. Die gleichen Probanden wie in Studie 2 absolvierten ein 6-tägiges intensives Trainingslager. Vor, direkt nach und nach weiteren 2 Tagen Pause absolvierten sie folgende Tests: TT, WT und ST im Abstand von je 2 Stunden. Aus dem ST wurden die oben genannten Schwellen bestimmt. Zur Bestimmung der Unterschiede zwischen den Testtagen wurde eine ANOVA mit Messwiederholung berechnet. Bei signifikanten Unterschieden wurde ein post-hoc Scheffé-Test durchgeführt. Die Probanden absolvierten TT an Tag 1 im Mittel in 3942 ± 212 s, an Tag 8 in 4008 ± 201 s und an Tag 11 in 3929 ± 219 s (p < 0,001). Ebenso waren die mittlere Leistung im WT zwischen Tag 1 (701 ± 58 W) und Tag 8 (679 ± 65 W) sowie zwischen Tag 8 und Tag 11 (696 ± 69 W) und die maximale Leistung im ST (338 ± 30 W; 327 ± 31 W; 347 ± 30 W) signifikant unterschiedlich (p < 0,01). Für die submaximalen Schwellen nach Dickhuth (p = 0,34) und 4-mmol (p = 0,69) konnte kein Einfluss der Ermüdung gefunden werden. Die Stegmann-Schwelle lag nach den Regenerationstagen höher als im ermüdeten Zustand (Tag 1 - 8: p = 0,85; Tag 8 - 11: p = 0,03). Dmax lag an Tag 8 (256 ± 27 W) niedriger als an Tag 1 (p = 0,006; 265 ± 29 W) und Tag 11 (p < 0,001; 269 ± 25 W). Damit ist diese Arbeit die erste, die zeigen konnte, dass Ermüdung keinen Einfluss auf das Ergebnis in den submaximalen Schwellenkonzepten nach Stegmann, Dickhuth und 4-mmol hat. Lediglich die Dmax-Methode weicht auf Grund reduzierter maximaler Ausbelastung im ermüdeten Zustand im Mittel mehr als die Grundvariabilität vom erholten Zustand ab.
... The maximal lactate steady state (MLSS), defined as the highest exercise intensity that can be maintained over time without a continual accumulation of [BLa-], has been widely accepted as the criterion by which LT2 should be identified [7][8][9][10] . Constant-load exercise above MLSS is associated with a constant increase in [BLa-] and V · O2, and is poorly tolerated by athletes for extended periods of time, therefore supporting MLSS as a strong indicator of the endurance capacity of athletes 2,9 . ...
... Constant-load exercise above MLSS is associated with a constant increase in [BLa-] and V · O2, and is poorly tolerated by athletes for extended periods of time, therefore supporting MLSS as a strong indicator of the endurance capacity of athletes 2,9 . However, measurement of MLSS is not easily assessed, requiring several constant-load tests to be performed with different intensities on different days and therefore, does not lend itself well to routine assessment 7,10 . While the determination of MLSS requires multiple test sessions, threshold indices are most commonly assessed using a single incremental exercise test with step durations <5 min 5,9,[11][12][13][14] . ...
... Training at intensities where [BLa-] remains stable may optimize aerobic training while also reducing the risk of overreaching, hence MLSS has been used to detect the upper limit or intensity for sustained endurance training 4,21 . Considering the additional time and associated financial costs required in the direct determination of MLSS, researchers have attempted to construct methods to directly measure it using simpler protocols 9,24 , or have attempted to draw conclusions from data derived from progressive incremental tests [4][5][6][7]9,10,17 . The present study compared data derived from high-level rowers performing a series of progressive incremental tests with MLSS assessed using 30-min constant-intensity trials and showed that valid estimates of MLSS can be determined from incremental tests. ...
Article
This study aimed to identify the minimum increment duration required to accurately assess two distinct lactate thresholds. Twenty-one elite rowers (12 female, 9 males) each performed 8-9 rowing tests comprising: 1) five progressive incremental tests (3, 4, 5, 7 or 10 min steps); and 2) at least three 30-min constant-intensity maximal lactate steady state (MLSS) assessments. Power output (PO) at lactate threshold 1 (LT1) was higher in the 3- and 4-min incremental tests. No other measures were different for LT1. PO at the second lactate threshold (LT2) was different between most tests and was higher than the PO at MLSS except for the 10-min incremental test. LT2 Oxygen consumption (V̇O2) was higher in the 3-, 4- and 5-min tests but heart rate (HR) and rating of perceived exhaustion were not different between tests. Peak PO in the incremental tests was inversely related to the step durations (r(2) = 0.86, p ≤ 0.02). Peak V̇O2 was higher in the shorter (≤ 5 min) compared with the longer incremental tests (≥ 7 min), while peak HR was not different between tests. These data suggest that for the methods used in this study: 1) incremental exercise tests with step durations ≤ 7 min overestimate MLSS exercise intensity; 2) Peak physiological values are best determined using incremental tests with step durations ≤ 4 min; and 3) HR measures are not affected by step duration and therefore prescription of training HRs can be made using any of these tests.
... To assess anaerobic threshold (AT) exist multiple methods, that are discussed controversially [1,2,3]. Usually lactate threshold concepts use the blood lactate concentration as the only parameter to approximate power in maximal lactate steady state (PMLSS). ...
... The maximal lactate steady state (MLSS) is defined as the highest endurance work load that can be maintained with stable blood lactate without further increase of more than 0.05 mmol•l -1 •min -1 within the last 20 min of a 30 min constant load test [2]. However, Hauser et al. [3] showed that the individual differences of power assessed using different lactate threshold concepts and power measured at MLSS was up to 56 W. This might be relevant in defining optimal training plans. Furthermore, Bleicher et al. [4] demonstrated that equal lactate curves and their corresponding permanent exercise levels may result from different combinations of maximal oxygen uptake ( · VO 2max ) and maximal lactate production rate ( · VLa max ) proving that two persons with equal AT have different metabolic parameters. ...
Article
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Complex performance diagnostics in sports medicine should contain maximal aerobic and maximal anaerobic performance. The requirements on appropriate stress protocols are high. To validate a test protocol quality criteria like objectivity and reliability are necessary. Therefore, the present study was performed in intention to analyze the reliability of maximal lactate production rate (V.Lamax) by using a sprint test, maximum oxygen consumption (V.O2max) by using a ramp test and, based on these data, resulting power in calculated maximum lactate-steady-state (PMLSS) especially for amateur cyclists. All subjects (n = 23, age 26 ± 4 years) were leisure cyclists. At three different days they completed first a sprint test to approximate V.Lamax. After 60 min of recreation time a ramp test to assess V.O2max was performed. The results of V.Lamax-test and V.O2max-test and the body weight were used to calculate PMLSS for all subjects. The intra class correlation (ICC) for V.Lamax and V.O2max was 0.904 and 0.987, respectively, coefficient of variation (CV) was 6.3% and 2.1%, respectively. Between the measurements the reliable change index of 0.11 mmol·l -1s -1 for V.Lamax and 3.3 mlkg -1min -1 for V.O2max achieved significance. The mean of the calculated PMLSS was 237 ± 72 W with an RCI of 9 W and reached with ICC = 0.985 a very high reliability. Both metabolic performance tests and the calculated PMLSS are reliable for leisure cyclists.
... In addition, FBLC thresholds allow the assessment of several athletes at the same time, are related to muscle fibre type distribution, capillary density and muscle enzyme activities (Jacobs, Sjödin, & Schele, 1983;Sjödin & Jacobs, 1981) and are highly reproducible (Borch, Ingjer, Larsen, & Tomten, 1993;Weltman et al., 1990). Unlike other lactate-related thresholds (Hauser, Adam, & Schulz, 2014;Monteiro de Barros, Mendes, Mortimer, Ramos, & Garcia, 2014), FBLC thresholds can most of the time be determined if the exercise test protocol is adapted to the participant's physical fitness. Moreover, FBLC thresholds have been shown to predict the MLSS int and running performance as well as, or better than, other lactate thresholds (Beneke, 1995;Hauser et al., 2014;Santos-Concejero et al., 2014). ...
... Unlike other lactate-related thresholds (Hauser, Adam, & Schulz, 2014;Monteiro de Barros, Mendes, Mortimer, Ramos, & Garcia, 2014), FBLC thresholds can most of the time be determined if the exercise test protocol is adapted to the participant's physical fitness. Moreover, FBLC thresholds have been shown to predict the MLSS int and running performance as well as, or better than, other lactate thresholds (Beneke, 1995;Hauser et al., 2014;Santos-Concejero et al., 2014). ...
Article
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This study aimed to validate the use of a single blood lactate concentration measure taken following a 12 km h(-1) running stage (BLC12) to predict and monitor fixed blood lactate concentration (FBLC) thresholds. Three complementary studies were undertaken. Study I: the relationships between BLC12 and the running speeds at FBLC of 3 mmol L(-1) (S3mM) and 4 mmol L(-1) (S4mM) measured during a multistage running field test were examined in 136 elite athletes. Study II: data from 30 athletes tested one year apart were used to test the predictive capacity of the equations obtained in Study I. Study III: 80 athletes were tested before and after an intensified training period to examine whether training-induced changes in FBLC thresholds could be predicted and monitored by BLC12. Study I: BLC12 was significantly (P < 0.001) and inversely related to S3mM (R(2) = 0.89) and S4mM (R(2) = 0.95). Study II: prediction models yielded robust correlations between the estimated and measured FBLC thresholds (r = 0.94-0.99; P < 0.001). Study III: estimated changes predicted actual training-induced changes in FBLC thresholds (r = 0.81-0.91; P < 0.001). This study gives empirical support to use a single lactate measure during a sub-maximal running field test as a simple, low-cost and practical alternative to FBLC thresholds in athletes.
... Different lactate threshold methods have been proposed for estimating MLSS, such as the IAT, or anaerobic thresholds at fixed [La -] of 3.5 and 4 mmol • L -1 [16, 19, 61, 62], but none of these methodologies have presented conclusive results. The differences in test protocols used in the original threshold investigations, the large individual differences shown by the lactate thresholds, and [La -] applied as references for the anaerobic threshold explain the discrepancies[26,63,64]. Probably, lactate threshold methods do not ac-curately estimate the intensity corresponding to MLSS, as the correspondence found between the fixed lactate concentrations and the intensity corresponding to MLSS may be due to a mere coincidence, and an overall interpretation of the result neglecting the individual differences[48,54,64]. ...
... Different lactate threshold methods have been proposed for estimating MLSS, such as the IAT, or anaerobic thresholds at fixed [La -] of 3.5 and 4 mmol • L -1 [16, 19, 61, 62], but none of these methodologies have presented conclusive results. The differences in test protocols used in the original threshold investigations, the large individual differences shown by the lactate thresholds, and [La -] applied as references for the anaerobic threshold explain the discrepancies[26,63,64]. Probably, lactate threshold methods do not ac-curately estimate the intensity corresponding to MLSS, as the correspondence found between the fixed lactate concentrations and the intensity corresponding to MLSS may be due to a mere coincidence, and an overall interpretation of the result neglecting the individual differences[48,54,64]. The range of [La -] at MLSS[2,7] and high day-to-day variability for lactate at MLSS[56] support the coincidental similarity between a given lactate value from incremental and constant intensity exercise. ...
Article
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The aim was to determine whether the midpoint between ventilatory thresholds (MPVT) corresponds to maximal lactate steady state (MLSS). Twelve amateur cyclists (21.0 ± 2.6 years old; 72.2 ± 9.0 kg; 179.8 ± 7.5 cm) performed an incremental test (25 W·min⁻¹) until exhaustion and several constant load tests of 30 minutes to determine MLSS, on different occasions. Using MLSS determination as the reference method, the agreement with five other parameters (MPVT; first and second ventilatory thresholds: VT1 and VT2; respiratory exchange ratio equal to 1: RER = 1.00; and Maximum) was analysed by the Bland-Altman method. The difference between workload at MLSS and VT1, VT2, RER=1.00 and Maximum was 31.1 ± 20.0, -86.0 ± 18.3, -63.6 ± 26.3 and -192.3 ± 48.6 W, respectively. MLSS was underestimated from VT1 and overestimated from VT2, RER = 1.00 and Maximum. The smallest difference (-27.5 ± 15.1 W) between workload at MLSS and MPVT was in better agreement than other analysed parameters of intensity in cycling. The main finding is that MPVT approached the workload at MLSS in amateur cyclists, and can be used to estimate maximal steady state.
... For example, Beneke [19] found marked differences in the workloads at LT and OBLA 4mM in an incremental test with respect to the workload at MLSS in high-level rowers. Recently, Hauser et al. [43], in male trained subjects during a different GXT protocol (40W/ 4min), found similar evidences to those described in our work. These authors detected differences between the results of LT+1.5 mMÁL -1 and MLSS (i.e., low validity values). ...
... However, contrary to our results, they found great similarities between the OBLA 4mM and MLSS values. The discrepancies between studies may be related to our faster increase in workload during the GXT protocol (40 W every 4 min for Hauser et al. [43], while 75 W every 4 min, presently). ...
Article
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Purpose The purpose of this study was to determine, i) the reliability of blood lactate and ventilatory-based thresholds, ii) the lactate threshold that corresponds with each ventilatory threshold (VT1 and VT2) and with maximal lactate steady state test (MLSS) as a proxy of cycling performance. Methods Fourteen aerobically-trained male cyclists (V˙O2max 62.1±4.6 ml·kg⁻¹·min⁻¹) performed two graded exercise tests (50 W warm-up followed by 25 W·min⁻¹) to exhaustion. Blood lactate, V˙O2 and V˙CO2 data were collected at every stage. Workloads at VT1 (rise in V˙E/V˙O2;) and VT2 (rise in V˙E/V˙CO2) were compared with workloads at lactate thresholds. Several continuous tests were needed to detect the MLSS workload. Agreement and differences among tests were assessed with ANOVA, ICC and Bland-Altman. Reliability of each test was evaluated using ICC, CV and Bland-Altman plots. Results Workloads at lactate threshold (LT) and LT+2.0 mMol·L⁻¹ matched the ones for VT1 and VT2, respectively (p = 0.147 and 0.539; r = 0.72 and 0.80; Bias = -13.6 and 2.8, respectively). Furthermore, workload at LT+0.5 mMol·L⁻¹ coincided with MLSS workload (p = 0.449; r = 0.78; Bias = -4.5). Lactate threshold tests had high reliability (CV = 3.4–3.7%; r = 0.85–0.89; Bias = -2.1–3.0) except for DMAX method (CV = 10.3%; r = 0.57; Bias = 15.4). Ventilatory thresholds show high reliability (CV = 1.6%–3.5%; r = 0.90–0.96; Bias = -1.8–2.9) except for RER = 1 and V-Slope (CV = 5.0–6.4%; r = 0.79; Bias = -5.6–12.4). Conclusions Lactate threshold tests can be a valid and reliable alternative to ventilatory thresholds to identify the workloads at the transition from aerobic to anaerobic metabolism.
... With the coming of a wide range of wearables for measuring the external load (speed, distance, etc.) and internal load (heart rate, blood/plasma lactate concentrations, heart rate variability, body temperature, gait symmetry, etc.), the first steps are made towards the objective assessment of performance capacity and individualisation of training programmes (Poole and Erickson, 2008;Peyré-Tartaruga and Coertjens, 2018;Impellizzeri et al., 2019). However, translating the obtained data and the derived performance parameters into effective training advice to induce the desired psychophysiological responses, is rarely applied in the different equestrian disciplines and still needs a lot of optimisation (Bourgela and Blais, 1991;Hauser et al., 2014;Arratibel-Imaz et al., 2016). For this purpose, reliable and reproducible SETs are needed under natural conditions that are easy to implement. ...
... Besides that, contradicting results can be found in scientific literature with respect to reproducibility, validation, and suitability of these tests to assess aerobic performance capacity for the different equestrian and human sports disciplines. Some studies report a good reproducibility (Dubreucq et al., 1995), whereas others report a lack of scientific evidence (Bourgela and Blais, 1991;Hauser et al., 2014;Arratibel-Imaz et al., 2016). A third and other important factor is the set of parameters that is chosen to indicate the lactate threshold. ...
Article
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There is a great need for objective external training load prescription and performance capacity evaluation in equestrian disciplines. Therefore, reliable standardised exercise tests (SETs) are needed. Classic SETs require maximum intensities with associated risks to deduce training loads from pre-described cut-off values. The lactate minimum speed (LMS) test could be a valuable alternative. Our aim was to compare new performance parameters of a modified LMS-test with those of an incremental SET, to assess the effect of training on LMS-test parameters and curve-shape, and to identify the optimal mathematical approach for LMS-curve parameters. Six untrained standardbred mares (3–4 years) performed a SET and LMS-test at the start and end of the 8-week harness training. The SET-protocol contains 5 increments (4 km/h; 3 min/step). The LMS-test started with a 3-min trot at 36–40 km/h [until blood lactate (BL) > 5 mmol/L] followed by 8 incremental steps (2 km/h; 3 min/step). The maximum lactate steady state estimation (MLSS) entailed >10 km run at the LMS and 110% LMS. The GPS, heartrate (Polar®), and blood lactate (BL) were monitored and plotted. Curve-parameters (R core team, 3.6.0) were (SET) VLa1.5/2/4 and (LMS-test) area under the curve (AUC>/ 0.80), Bland-Altman method, and ordinary least products (OLP) regression analyses were determined for test-correlation and concordance. Training induced a significant increase in VLa1.5/2/4. The width of the AW increased significantly while the AUC>LMS and LMS decreased post-training (flattening U-curve). The LMS BL steady-state is reached earlier and maintained longer after training. BLmax was significantly lower for LMS vs. SET. The 40° angular method is the optimal approach. The correlation between LMS and VMLSS was significantly better compared to the SET. The VLa4 is unreliable for equine aerobic capacity assessment. The LMS-test allows more reliable individual performance capacity assessment at lower speed and BL compared to SETs. The LMS-test protocol can be further adapted, especially post-training; however, inducing modest hyperlactatemia prior to the incremental LMS-stages and omitting inclusion of a per-test recovery contributes to its robustness. This LMS-test is a promising tool for the development of tailored training programmes based on the AW, respecting animal welfare.
... PMLSS is defined as the highest workload where lactate-formation and lactate-elimination in the muscle cell are maintained at a steady-state [2][3][4]. However, Hauser et al. [5] compared the power at "onset of blood lactate accumulation" (OBLA) [6,7], the "individual anaerobic threshold" (IAT) [8] and the " + 1.5 mmol·l −1 lactate model" [9] with power in MLSS, measured during 30-minutes constant load tests. They found high significant correlations between OBLA and MLSS: r = 0.89 (mean difference −7.4 W); IAT and MLSS: r = 0.83 (mean difference 12.4 W), +1.5 mmol·l −1 lactate model and MLSS: r = 0.88 (mean difference −37.4 W). ...
... Heck [4] also evaluated correlations between MLSS and OBLA and individual anaerobic threshold of r = 0.92 and r = 0.87, respectively. However, as already mentioned, the investigation of Hauser et al. [5] showed large individual differences comparing power of threshold-concepts with power in MLSS. Therefore the calculation method is at least as useful the application of lactate-concepts to detect MLSS. ...
Article
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Background The purpose of this study was the comparison of the calculated (MLSSC) and experimental power (MLSSE) in maximal lactate steady-state (MLSS) during cycling. Methods 13 male subjects (24.2 ± 4.76 years, 72.9 ± 6.9 kg, 178.5 ± 5.9 cm, V˙O2max: 60.4 ± 8.6 ml min−1 kg−1, V˙Lamax: 0.9 ± 0.19 mmol l-1 s-1) performed a ramp-test for determining the V˙O2max and a 15 s sprint-test for measuring the maximal glycolytic rate (V˙Lamax). All tests were performed on a Lode-Cycle-Ergometer. V˙O2max and V˙Lamax were used to calculate MLSSC. For the determination of MLSSE several 30 min constant load tests were performed. MLSSE was defined as the highest workload that can be maintained without an increase of blood-lactate-concentration (BLC) of more than 0.05 mmol l−1 min−1 during the last 20 min. Power in following constant-load test was set higher or lower depending on BLC. Results MLSSE and MLSSC were measured respectively at 217 ± 51 W and 229 ± 47 W, while mean difference was −12 ± 20 W. Orthogonal regression was calculated with r = 0.92 (p < 0.001). Conclusions The difference of 12 W can be explained by the biological variability of V˙O2max and V˙Lamax. The knowledge of both parameters, as well as their individual influence on MLSS, could be important for establishing training recommendations, which could lead to either an improvement in V˙O2max or V˙Lamax by performing high intensity or low intensity exercise training, respectively. Furthermore the validity of V˙Lamax -test should be focused in further studies.
... However, this model is based on studies that used lactate thresholds (LT) to determine the beginning of blood lactate accumulation (Costill et al. 1973;Allen et al. 1985;Coyle et al. 1988Coyle et al. , 1991. Depending on the threshold concepts and the incremental rate used during exercise tests lactate thresholds under-or overestimate the highest intensity that can be sustained without continuous accumulation of blood lactate during prolonged exercise (Heck et al. 1985;Beneke 1995;Hauser et al. 2013;Jamnick et al. 2018). The highest blood lactate concentration (BLC) increasing by no more than 1.0 mmol·L −1 during the final 20 min of a 30-min constant load bout is termed maximal lactate steady state (MLSS) (Beneke 1995(Beneke , 2003. ...
Article
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Purpose There is no convincing evidence for the idea that a high power output at the maximal lactate steady state (PO_MLSS) and a high fraction of 𝑉˙O2max at MLSS (%𝑉˙O2_MLSS) are decisive for endurance performance. We tested the hypotheses that (1) %𝑉˙O2_MLSS is positively correlated with the ability to sustain a high fraction of 𝑉˙O2max for a given competition duration (%𝑉˙O2_TT); (2) %𝑉˙O2_MLSS improves the prediction of the average power output of a time trial (PO_TT) in addition to 𝑉˙O2max and gross efficiency (GE); (3) PO_MLSS improves the prediction of PO_TT in addition to 𝑉˙O2max and GE. Methods Twenty-one recreationally active participants performed stepwise incremental tests on the first and final testing day to measure GE and check for potential test-related training effects in terms of changes in the minimal lactate equivalent power output (∆PO_LEmin), 30-min constant load tests to determine MLSS, a ramp test and verification bout for 𝑉˙O2max, and 20-min time trials for %𝑉˙O2_TT and PO_TT. Hypothesis 1 was tested via bivariate and partial correlations between %𝑉˙O2_MLSS and %𝑉˙O2_TT. Multiple regression models with 𝑉˙O2max, GE, ∆PO_LEmin, and %𝑉˙O2_MLSS (Hypothesis 2) or PO_MLSS instead of %𝑉˙O2_MLSS (Hypothesis 3), respectively, as predictors, and PO_TT as the dependent variable were used to test the hypotheses. Results %𝑉˙O2_MLSS was not correlated with %𝑉˙O2_TT (r = 0.17, p = 0.583). Neither %𝑉˙O2_MLSS (p = 0.424) nor PO_MLSS (p = 0.208) did improve the prediction of PO_TT in addition to 𝑉˙O2max and GE. Conclusion These results challenge the assumption that PO_MLSS or %𝑉˙O2_MLSS are independent predictors of supra-MLSS PO_TT and %V˙O2_TT.
... However, MLSS determination is time-consuming and is usually replaced by single-session tests. Certainly, these single-session tests are differently handicapped by impaired validity, accuracy, resolution, or reliability [1,15]. Most existing single-session tests use either fixed lactate concentrations [17,21] or transition/inflection points [3,7] as determination criteria for MLSS. ...
... L −1 ) has been reproduced as a valid estimation of the MLSS based on 2 studies that recruited trained cyclists and employed a GXT with 3-min stages [14,158]. However, this result could not be confirmed with any other GXT stage length [14,151,162]. The D max [71] and Modified D max [72] methods are curve-fitting LT models that, despite no evidence to support the validity of the original methods to identify the MLSS, or to delineate the heavy and severe domains, remain staple LT methods. ...
Article
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Prescribing the frequency, duration, or volume of training is simple as these factors can be altered by manipulating the number of exercise sessions per week, the duration of each session, or the total work performed in a given time frame (e.g., per week). However, prescribing exercise intensity is complex and controversy exists regarding the reliability and validity of the methods used to determine and prescribe intensity. This controversy arises from the absence of an agreed framework for assessing the construct validity of different methods used to determine exercise intensity. In this review, we have evaluated the construct validity of different methods for prescribing exercise intensity based on their ability to provoke homeostatic disturbances (e.g., changes in oxygen uptake kinetics and blood lactate) consistent with the moderate, heavy, and severe domains of exercise. Methods for prescribing exercise intensity include a percentage of anchor measurements, such as maximal oxygen uptake ($${\dot{\text{V}}\text{O}}_{{{\text{2max}}}}$$), peak oxygen uptake ($${\dot{\text{V}}\text{O}}_{{{\text{2peak}}}}$$), maximum heart rate (HRmax), and maximum work rate (i.e., power or velocity—$${\dot{\text{W}}}_{{\max}}$$ or $${\dot{\text{V}}}_{{\max}}$$, respectively), derived from a graded exercise test (GXT). However, despite their common use, it is apparent that prescribing exercise intensity based on a fixed percentage of these maximal anchors has little merit for eliciting distinct or domain-specific homeostatic perturbations. Some have advocated using submaximal anchors, including the ventilatory threshold (VT), the gas exchange threshold (GET), the respiratory compensation point (RCP), the first and second lactate threshold (LT1 and LT2), the maximal lactate steady state (MLSS), critical power (CP), and critical speed (CS). There is some evidence to support the validity of LT1, GET, and VT to delineate the moderate and heavy domains of exercise. However, there is little evidence to support the validity of most commonly used methods, with exception of CP and CS, to delineate the heavy and severe domains of exercise. As acute responses to exercise are not always predictive of chronic adaptations, training studies are required to verify whether different methods to prescribe exercise will affect adaptations to training. Better ways to prescribe exercise intensity should help sport scientists, researchers, clinicians, and coaches to design more effective training programs to achieve greater improvements in health and athletic performance.
... According to Hauser et al. (2014b), the MLSS is mainly influenced by VO 2max and dLa/dt max . While VO 2max and its influence on endurance performance has been focused on in most endurance studies (Coyle et al., 1988;Schumacher and Mueller, 2002;Støren et al., 2012), dLa/dt max has been neglected and appears to be an underestimated parameter in terms of the origin and interpretation of MLSS (Mader and Heck, 1986). ...
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Aim: To monitor the training intensity distribution (TID) and the development of physiological and performance parameters. Methods: During their preparation period for the RAAM, 4 athletes (plus 1 additional backup racer) performed 3 testing sessions; one before, one after 3, and one after 6 months of training. VO2max, maximal rate of lactate accumulation (dLa/dt(max)), critical power, power output at lactate minimum (MLSSp), peak and mean power output during a sprint test, heart rate recovery, isometric strength, jumping height, and body composition were determined. All training sessions were recorded with a power meter. The endurance TID was analyzed based on the time in zone approach, according to a classical 3-zone model, including all power data of training sessions, and a power specific 3-zone model, where time with power output below 50% of MLSSp was not considered. Results: The TID using the classical 3-zone model reflected a pyramidal TID (zone 1: 63 16, zone 2: 28 13 and zone 3: 9 4%). The power specific 3-zone model resulted in a threshold-based TID (zone 1: 48 13, zone 2: 39 10, zone 3: 13 4%). VO2max increased by 7.1 +/- 5.3% (P = 0.06). dLa/dt(max), decreased by 16.3 +/- 8.1% (P = 0.03). Power output at lactate minimum and critical power increased by 10.3 +/- 4.1 and 16.8 +/- 6.2 % (P = 0.01), respectively. No changes were found for strength parameters and jumps. Conclusion: The present study underlines that a threshold oriented TID results in only moderate increases in physiological parameters. The amount of training below 50% of MLSSp (similar to 28% of total training time) is remarkably high. Researchers, trainers, and athletes should pay attention to the different ways of interpreting training power data, to gain realistic insights into the TID and the corresponding improvements in performance and physiological parameters.
... However, MLSS determination is time-consuming and is usually replaced by single session tests. Certainly, these single session tests are differently handicapped by impaired validity, accuracy, resolution, or reliability (2,14). Most existing single-session tests use either fixed lactate concentrations (15,21) or transition/inflection-points (6,9) as determination criteria for MLSS. ...
Article
This study evaluated the accuracy of a modified-lactate-minimum-test (mLMT), a modified-reverse-lactate-threshold-test (mRLT), in comparison to two established threshold concepts (OBLA & mDmax) to determine power output at maximal-lactate-steady-state (MLSS) in cycling. Nineteen subjects performed an mLMT, an mRLT, a graded-exercise-test (100W start, +20W every 3 min) and 3 or more constant-load tests of 30 minutes to determine power output at MLSS. The mLMT and mRLT both consisted of an initial lactate priming-segment, followed by a short recovery phase. Afterwards, the initial load of the subsequent incremental- or reverse-segment was calculated individually and was increased or decreased by 10W every 90 sec respectively. The mean difference to MLSS was +2 ± 7W (mLMT), +5 ± 10W (mRLT), +9 ± 21W (OBLA) and +6 ± 14W (mDmax). The correlation between power output at MLSS and mLMT was highest (r=0.99), followed by mRLT (r=0.98), mDmax (r=0.95) and OBLA (r=0.90). Due to the higher accuracy of the mLMT and the mRLT to determine MLSS compared to OBLA and mDmax, we suggest both tests as valid and meaningful concepts to estimate power output at MLSS in one single test in moderately up to well-trained athletes. Additionally, our modified tests provide anaerobic data and do not require detailed knowledge of the subjects' training status compared to previous LMT- or RLT-protocols.
... 1 Fixed blood lactate concentration (FBLC) thresholds, such as the running speeds associated with 3 mmol·L −1 (S3mM) and 4 mmol·L −1 (S4mM, also termed OBLA), are often preferred over other lactate thresholds. [2][3][4][5] FBLC thresholds, indeed, (a) reduce the time and cost of the assessment procedure 3 ; (b) are measurable both individually in laboratory settings and in several athletes at the same time in field settings 6 ; (c) are feasible procedures to incorporate into the training monitoring of competitive athletes 5 ; (d) are related to muscle fiber type distribution and capillary density 7 ; (e) are highly reproducible 8 ; (f) unlike other lactate thresholds, are usually easy to determine 9 ; and (g) predict running performance as well as or better than other lactate thresholds. 10 Determination of FBLC thresholds, however, is an invasive procedure requiring qualified personnel to perform several blood sample extractions per subject, plus the subsequent blood analysis. ...
... This could cause great error when the results are used for other techniques, such as double poling. Since the MLSSv is widely used as a parameter in training prescription by coaches, a good agreement between AnT concepts and MLSS is essential (25). ...
Article
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The response of blood lactate concentration (BLC) to exercise is a commonly used approach to set training intensities and to determine the anaerobic threshold, which are important in evaluation of endurance exercise performance. The maximal lactate steady state (MLSS) is defined as the highest workload or BLC that can be maintained without continual lactate accumulation over time. The aim of this study was to investigate MLSS in the cross-country skiing sub-technique double poling and to assess the validity of a fixed blood lactate threshold (OBLA and the 45° tangent of the lactate curve). Eight well-trained cross-country skiers (age = 27.6±8.8 years [mean±SD], body mass = 73.9±6.2 kg, height = 179.3±7.0 cm) performed an incremental test to determine OBLA and Individual Anaerobic Threshold (IAnT) and several constant workload tests of 30 min to determine the MLSS. Lactate concentration at MLSS in double poling was 6.7±1.3 mmol ·L⁻¹ which was significantly higher compared to OBLA (p<0.001) and IAnT (p<0.01). Despite significant correlations in velocities between MLSS-IAnT and MLSS-OBLA (r=0.95/0.95, p<0.001), significant (p<0.01) differences between MLSS (21.4±2.8 km ·h⁻¹) versus IAnT (20.6±3.6 km ·h⁻¹) and OBLA (19.9±3.0 km ·h⁻¹) was observed. It was concluded that both OBLA and IAnT underestimate MLSS in double poling. A fixed value of 7 mmol ·L⁻¹ would be more appropriate in lactate testing of cross-country skiers using the double poling technique, yet dissuaded because of intra-individual variations. Direct determination of MLSS is the recommended approach for useful exercise thresholds, important for training interventions in elite cross-country skiers.
... Another disadvantage is the rarely reported validation of AT's by the MLSS [7], which can be determined reliably and with low day-to-day variability [24]. Hauser et al. [25] compared Dickhuth's threshold with MLSS in cycling. However, they compared the power instead of the heart rate. ...
Article
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Intensity description as exercise prescription is a main challenge for sport scientists and coaches. Most commonly used in endurance sports are percentages of the heart rate at Maximal Lactate Steady State (MLSS). Since the late 1970s, MLSS is approximated by several anaerobic thresholds based on lactate extraction during graded incremental tests. Since then, scientists look for non-invasive methods to approximate these thresholds. Our new approach uses the sports computer science Performance Potential model (PerPot) for determination. The antagonistic model PerPot models the relation between load (speed) and performance (heart rate). This investigation compares lactate based and PerPot simulated thresholds. Fifteen male handball players performed a graded incremental test with lactate extraction and continuous heart rate recording. Lactate measurement was used for determination of four established lactate-based anaerobic thresholds (OBLA, Dickhuth, Keul, Simon). Speed and heart rate processes were used for PerPot determination of the threshold. Both Pearson correlations (r=0.883 – r=0.895) and intraclass correlations (ICC=0.894 – ICC=0.932) show high correlations between lactate-based and PerPot simulated thresholds. Using anaerobic threshold (AT) for exercise prescription is the ideal case. Because of adaptation, AT changes and should therefore be determined periodically. In practice, this is rarely done because of high cost and difficult invasive determination in laboratory. PerPot provides a low cost, non-invasive method for AT determination. It is therefore an ideal method for verifying former results of sports medicine diagnostics periodically. In addition, simple portability of results to field is an advantage, because the graded incremental tests can also be performed outdoors.
... To investigate this, one would have to study the correlations between measured values for the power in the MLSS and our characteristic power scales. This was done in the past for the traditional "lactate thresholds" (Heck et al. 1985, Lajoie et al. 2000, van Schuylenbergh et al. 2004, Beneke 1995, Jones and Doust 1998, Hauser et al. 2014) and requires as many data sets as possible. The bottleneck is the time-consuming precise determination of the power in the MLSS, which can only be obtained in a series of (at least) 30 minutes constant load tests. ...
Preprint
Purpose: Measuring the blood lactate concentration allows for a glimpse at the metabolic processes during exercise. To extract characteristics of metabolism the relationship between blood lactate concentration and power or velocity is modeled. Current modeling approaches allow only limited interpretation, are in conflict with basic principles of scientific mathematical modeling, and lack a phenomenological reasoning. Methods: We developed a simple analytical expression to model lactate concentration data from graded incremental exercise tests. We compared our new approach to a traditional one in a dataset of N = 24 exercise tests performed by elite junior triathletes. Results: The new procedure leads to three independent fitting parameters characterizing the baseline lactate concentration, the intensity (power, velocity) at the onset as well as the rate of increase of the lactate concentration. These parameters have a clear meaning and can directly be used for diagnostics. They can be interpreted with more confidence compared to the characteristics extracted in the traditional approach. Conclusion: The performance indicators, naturally appearing in our modeling, should supersede the single points obtained from the traditional evaluation of graded incremental exercise tests (“lactate thresholds”), which can hardly be justified based on the principles of scientific mathematical modeling.
... The authors stated as a limitation to their study that the participants were not afforded familiarisation trials with the FTP 20 test. Furthermore, the relationship between the IAT and the MLSS has been questioned in other studies (Beneke, 1995;Hauser et al., 2014;Schuylenbergh et al., 2004). . ...
Article
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Functional Threshold Power (FTP) in cycling is increasingly used in exercise prescription, particularly with the rise in use of home trainers and virtual exercise platforms. FTP testing does not require biological sampling and is considered a more practical test than others. This scoping review investigated what is known about the 20-minute FTP (FTP²⁰) test. A three-step search strategy was used to identify studies in relevant databases (PubMed, CINAHL, SportDiscus, Google Scholar, Web of Science) and grey literature. Data were extracted and common themes identified which allowed for descriptive analysis and thematic summary. Fifteen studies were included. The primary focus fitted broadly into four themes: reliability, association with other physiological markers, other power-related concepts and performance prediction. The FTP²⁰ test was reported as a reliable test. Studies investigating the relationship of FTP²⁰ with other physiological markers and power-related concepts reported large limits of agreement suggesting parameters cannot be used interchangeably. Some findings indicate that FTP²⁰ may be useful in performance prediction. The majority of studies involved trained male cyclists. Overall, existing literature on the FTP²⁰ test is limited. Further investigation is needed to provide physiological justification for FTP²⁰ and inform use in exercise prescription in a range of populations.
... However, MLSS determination is time-consuming and is usually replaced by single-session tests. Certainly, these single-session tests are differently handicapped by impaired validity, accuracy, resolution, or reliability [1,15]. Most existing single-session tests use either fixed lactate concentrations [17,21] or transition/inflection points [3,7] as determination criteria for MLSS. ...
Article
This study evaluated the accuracy of the lactate minimum test, in comparison to a graded-exercise test and established threshold concepts (OBLA and mDmax) to determine running speed at maximal lactate steady state. Eighteen subjects performed a lactate minimum test, a graded-exercise test (2.4 m·s−1 start,+0.4 m·s−1 every 5 min) and 2 or more constant-speed tests of 30 min to determine running speed at maximal lactate steady state. The lactate minimum test consisted of an initial lactate priming segment, followed by a short recovery phase. Afterwards, the initial load of the subsequent incremental segment was individually determined and was increased by 0.1 m·s−1 every 120 s. Lactate minimum was determined by the lowest measured value (LMabs) and by a third-order polynomial (LMpol). The mean difference to maximal lactate steady state was+0.01±0.14 m·s−1 (LMabs), 0.04±0.15 m·s−1 (LMpol), –0.06±0.31 m·s1 (OBLA) and –0.08±0.21 m·s1 (mDmax). The intraclass correlation coefficient (ICC) between running velocity at maximal lactate steady state and LMabs was highest (ICC=0.964), followed by LMpol (ICC=0.956), mDmax (ICC=0.916) and OBLA (ICC=0.885). Due to the higher accuracy of the lactate minimum test to determine maximal lactate steady state compared to OBLA and mDmax, we suggest the lactate minimum test as a valid and meaningful concept to estimate running velocity at maximal lactate steady state in a single session for moderately up to well-trained athletes.
... Assim, os resultados do presente estudo parecem ser difíceis de serem aceitos como validos devido ao fato de que intensidades apenas ~5% acima da MFEL o 75 estado de equilíbrio fisiológico (i. e.[La]) já não ocorre(Figura 15), bem como, alguns sujeitos entram em exaustão antes de 30 min(BENEKE, 2003a;JONES, 2002).Porém, ao analisarmos uma série de estudos que testaram a validade de vários métodos do LAn determinados a partir das respostas de[La] ou respostas ventilatórias (VT2) em predizer a MFEL verificam-se os 95% LdC entre ~9,5 a 43%(ARRATIBEL-IMAZ et al., 2016;GROSSL et al., 2012b;ADAM;PALLARÉS et al., 2016;SMEKAL et al., 2012). Ainda sobre testes usados na predição da MFEL os LdC = 8,6 a 19,0% a partir da potência crítica(KEIR et al., 2015;MATTIONI MATURANA et al., 2016;JONES, 2002) e os LdC = 8,6% para o teste de 30 min de intensidade autosselecionada(MATTIONI MATURANA et al., 2017b). ...
Thesis
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... A large number of authors have tried to validate other tests to estimate the MLSS workload using incremental graded exercises test and [La] analysis. Hauser, Adam, and Schulz (2014) reported significant correlations between MLSS and the "onset of blood lactate accumulation (OBLA 4mmol )" (Sjodin and Jacobs 1981), "the individual anaerobic threshold (IAT)" (Jones and Doust 1998), and the "+ 1.5 mmol·L -1 lactate model" ( Dickhuth et al. 1999) (r = 0.89; r = 0.83 and r = 0.88, respectively), but with large individual differences based on the Bland-Altman model. also found high coefficient of correlation between Lactate threshold+0.5 and OBLA 4mmol and MLSS (r > 0.78, p < 0.05 in all cases). ...
Article
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... Here, the upper boundary corresponds to the maximal lactate steady state (Fig. 2d). It should, however, be noted that maximal lactate steady state tests have great variability [43] in terms of [La] but they are very repeatable in terms of power output [84], meaning that any correspondence with fixed [La] values of 2 and 4 mM is a simplification of the likely response. In the case of severe exercise, the [La] continues increasing ) and whole body energy turnover is considerably larger than that indicated by the V O 2 by an amount that is proportional to the net rate of lactate turnover [6]. ...
Article
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Purpose Bioenergetic models are used in cycling to estimate the acute physiological response in terms of oxygen consumption ($${\dot{\text{V}}}$$O2) and lactate concentration ([La]). First, our aim is to review the bioenergetic modelling literature, presenting historical evolution of concepts, techniques and related limitations. Second, our aim is to discuss how and where new approaches can stem and evolve. Methods This is a narrative review, where different modelling solutions are compared and qualitatively discussed. First, the principal features of the $${\dot{\text{V}}}$$O2 and [La] kinetics are presented, and then the models available in the literature are compared in light of what aspects of the physiological responses they can describe. Results Currently, models can detect most features of $${\dot{\text{V}}}$$O2 and [La] kinetics, but no single existing model appears appropriate for every exercising conditions. Limitations hindering the creation of an ultimate model are: the large variability of an exercise, the required mathematical complexity, and lack of reliable physiological data. To overcome these issues, new modelling solutions are being explored in the emerging AI technologies. However, in AI-models, parameters do not have direct physiological meaning and require massive amounts of experimental data for parameter calibration. Conclusions Despite the great efforts made by model developers and exercise physiologists, universal modelling solutions for the variety of potential exercising conditions remain unavailable. At present, further research is needed to assess the accuracy and predictive power of AI models to move the method forward in our field, as it is being done so in many others.
... As a result, it is not accurate to define a particular type of exercise, such as running, as aerobic or anaerobic, because the threshold at which lactate accumulates in the blood is dependent on the individual, and hence can only be determined with physiological testing (55). However, in daily usage and even in the scientific literature it has become commonplace to use both terms to refer to particular exercise types. ...
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Background: In the past three decades, there has been an increase in the number of studies assessing exercise as a form of treatment for substance use disorders (SUDs). While a variety of substance types and outcomes have been assessed, exercise intensities have never been systematically examined. Consequently, it remains unclear whether particular forms of exercise are better suited to the treatment of these populations. Anaerobic exercise has been shown to have positive effects in populations with psychiatric disorders, but its effectiveness in the treatment of SUDs has to date not been reviewed. Methods: The aim of this systematic review is to identify and evaluate studies which have employed either an acute or chronic anaerobic exercise component as a therapy modality for SUDs. The primary outcomes are abstinence, craving, withdrawal, consumption, quality of life, and the following psychological symptoms and disorders: depression, anxiety, stress, and mood. A secondary objective is to assess whether the type of training described in the study protocol can be reliably categorised as anaerobic training. Results: Twenty-six studies are included in this review. Twelve studies addressed nicotine dependence, one addressed alcohol dependence, and thirteen addressed dependence on various illicit drugs. Thirteen studies reported the intensity at which participants actually exercised, but only one employed a test to determine whether training was carried out above the anaerobic threshold. The risk of bias in the included studies was generally high. Results of the studies were mixed, with the most positive effects being found for abstinence in nicotine dependence. Conclusion: The evidence for the effects of anaerobic exercise in SUDs is weak, although a tendency towards positive effects on abstinence in nicotine dependent individuals was observable. The majority of studies do not report data on exercise intensity, making a categorisation of anaerobic exercise impossible in all but one case. This means that the effects of this form of exercise cannot be determined, and therefore not evaluated or compared with other forms. In order to improve the quality of evidence for exercise in SUD treatment, clearly defined and objectively assessed evaluations of anaerobic and anaerobic exercise are necessary.
... 2,27 However, the most common test to predict MLSS is the incremental test using AnT. 1 or ventilatory responses in predicting MLSS, the ±95% LoA, which accounts for 95% of individual differences between measures, 24 ranged from 9.5% to 43%. 16,[28][29][30][31] Continuing with the MLSS prediction tests, critical power (CP) is commonly used and presents a ±95% LoA of 8.6% to 19.0%. 27,32,33 Therefore, the results for random errors of prediction from FTP20 are near or lower than those commonly found in the literature for methods used to predict MLSS. ...
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Purpose: Functional Threshold Power (FTP), determined as 95% of the average power during a 20-minute time-trial test, is suggested as a practical test for the determination of the maximal lactate steady state (MLSS) in cycling. Therefore, the objective of the present study was to determine the validity of FTP in predicting MLSS. Method: Fifteen cyclists, 7 classified as trained and 8 as well-trained (mean ± standard deviation; maximal oxygen uptake = 62.3 ± 6.4 mL/kg/min, maximal aerobic power = 329 ± 30 Watts), performed an incremental test to exhaustion, an FTP test, and several constant load tests to determine the MLSS. The bias ± 95% limits of agreement (LoA), typical error of the estimate (TEE), and Pearson´s coefficient of correlation (r) were calculated to assess validity. Results: For the power output measures, FTP presented a bias ± 95% LoA of 1.4 ± 9.2%, a moderate TEE (4.7%), and nearly perfect correlation (r = 0.91) with MLSS in all cyclists together. When divided by the training level, the bias ± 95% LoA and TEE were higher in the trained group (1.4 ± 11.8% and 6.4%, respectively) than in the well-trained group (1.3 ± 7.4% and 3.0%, respectively). For the heart rate measurement, FTP presented a bias ± 95% LoA of −1.4 ± 8.2%, TEE of 4.0%, and very -large correlation (r = 0.80) with MLSS. Conclusion: Therefore, trained and well-trained cyclists can use FTP as a noninvasive and practical alternative to estimate MLSS
... However, MLSS determination is tedious and time-consuming (24). In field testing of team-sports, fixed lactate thresholds, such as running speeds associated with 3 mmol$L 21 (S3mM) and 4 mmol$L 21 (S4mM), which is also termed OBLA (34), are often preferred to MLSS (9,12,22,25) because they reduce the time and cost of the assessment procedure, are easy to measure in several athletes at the same time in field settings, and have been shown to reflect the speed at the MLSS as appropriate as other lactate thresholds (3,13). Unfortunately, the determination of the fixed lactate thresholds requires qualified personnel and involves blood sampling, which is an invasive technique that can be aversive to some participants. ...
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The aim of this study was to investigate whether the speed associated with 90% of maximal heart rate (S90%HRmax) could predict speeds at fixed blood lactate concentrations of 3 mmol·L (S3mM) and 4 mmol·L (S4mM). Professional team-sport players of futsal (n = 10), handball (n = 16) and basketball (n = 10) performed a four-stage discontinuous progressive running test followed, if exhaustion was not previously achieved, by an additional maximal continuous incremental running test to attain maximal heart rate (HRmax). The individual S3mM, S4mM and S90%HRmax were determined by linear interpolation. S3mM (11.6 ± 1.5 km·h) and S4mM (12.5 ± 1.4 km·h) did not differ (p > 0.05) from S90%HRmax (12.0 ± 1.2 km·h). Very large significant (p < 0.001) relationships were found between S90%HRmax and S3mM (r = 0.82; SEE = 0.87 km·h), as well as between S90%HRmax and S4mM (r = 0.82; SEE = 0.87 km·h). S3mM and S4mM inversely correlated with %HRmax associated with running speeds of 10 and 12 km·h (r = 0.78 - 0.81; p < 0.001; SEE = 0.94 - 0.87 km·h). In conclusion, S3mM and S4mM can be accurately predicted by S90%HRmax in professional team-sport players.
Article
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Chapter
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Sportmedizinische Leistungsprüfverfahren haben als wesentliche Aufgaben die Überprüfung der Gesundheit und der Sport- und Belastungstauglichkeit von Athleten/innen sowie die Feststellung des aktuellen Leistungszustandes unter standardisierten Bedingungen als Grundlage für weiterführende sportmedizinische und trainingspraktische Entscheidungen. Sportmedizinische Leistungsdiagnostik bestimmt dabei die Größe, die Richtungen und die Dynamik der inneren Beanspruchung bei definierten und standardisierten Belastungen und überprüft die physiologischen und patho-physiologischen Reaktionen auf standardisierte ergometrische Belastungen unter Verwendung maximaler und submaximaler Kennwerte.
Thesis
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Handcycling is an efficient and aerobically demanding exercise for improving endurance in individuals with an spinal cord injury (SCI) or amputation of the lower limb/s. Even though handcycling was found to be mechanically less straining when compared to manual wheelchair propulsion, Paralympic athletes are prone to overuse injuries of the upper extremity. Physiological aspects of handcycling exercise during cross-sectional and longitudinal studies have primarily investigated aerobic metabolism in terms of maximal oxygen consumption (V̇O2max) and efficiency. Biomechanical aspects of handcycling propulsion demonstrated alterations due to the handbike setup and different intensities. However, studies combining crank kinetics, joint kinematics and muscular activity are primarily based on single-case studies. Hence, this thesis aimed to assess anaerobic metabolism in terms of lactate kinetics and maximal lactate accumulation rate (V̇Lamax) and examine the complex biomechanics underlying handcycling propulsion in several participants. Two studies were performed in n = 12 and n = 18 able-bodied triathletes, respectively. In the first study, lactate kinetics, crank kinetics, joint kinematics and muscular activity were measured during three exercise modalities: an incremental step test until volitional exhaustion, a 15-s all-out sprint test and a 30-min continuous load trial at the individual lactate threshold (P4). The tests were performed in a recumbent racing handcycle (Shark S, Sopur, Sunrise Medical, Malsch, Germany) that was mounted on an ergometer (8 Hz, Cyclus 2, RBM electronic automation GmbH, Leipzig, Germany). Lactate kinetics were determined by using an enzymatic-amperometric sensor chip system (Biosen C-Line, EKF-diagnostics GmbH, Barleben, Germany) and adequate interpolation approaches. Tangential crank torque was measured using a power meter (1000 Hz, Schoberer Rad Messtechnik GmbH, Jülich, Germany) installed in the crank. Joint kinematics of the shoulder, elbow, wrist and trunk were calculated according to the Upper Limb Model of Vicon Nexus and ISB recommendation by using a 3D motion capturing system (100 Hz, Vicon Nexus 2.3, Vicon Motion Systems Ltd., Oxford, UK). Surface electromyography (sEMG) was performed for ten muscles of the upper extremity and trunk using a wireless sEMG system (1000 Hz, DTSEMG Sensor®, Noraxon Scottsdale, Arizona, USA). Additionally, different sEMG normalisation procedures were compared to determine adequate maximal voluntary isometric contraction (MVIC) positions. In the second study, peak power output and V̇Lamax were compared between handcycling and conventional (leg) cycling in terms of reliability, differences between and correlations among extremities. V̇Lamax was identified as a promising parameter in handcycling exercise testing, since V̇Lamax attained high reliability and correlated with both aerobic and anaerobic performance. Moreover, V̇Lamax was found to be extremity-specific which might be relevant for exercise testing in endurance sports with an emphasis on both extremities (e. g. rowing and cross-country skiing). Based on the biomechanical measurements, the pull phase was found to increase in work distribution with exercise intensity and duration. The muscular activation patterns (MAPs) of the examined muscles were used to identify their function in propulsion cycle and assess their sensitivity to fatigue. As the initiator of the pull phase, the posterior part of M. deltoideus (DP) was found to be most affected by exercise intensity and duration which highlights the necessity for additional conditioning. Whereas some muscles can be normalised by sport-specific MVICs, some muscles should be normalised muscle-specifically. Future studies should replicate these studies, examine the effect of deliberate training on V̇Lamax and investigate handcycling biomechanics in several elite SCI handcyclists/paratriathletes.
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Thesis
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The current Ph.D. dissertation revolves around the determination of aerobic capacity, with special reference to the development of practical strategies to overcome some limitations that sport scientific practitioners usually encounter at the time to give scientific endurance support to athletes or physical activity practitioners. This doctoral thesis is based on 4 scientific studies that have been published or accepted for publication in scientific international journals. The first study (Chapter 2) is a methodological laboratory-based study concerning one of the most classical and fundamental measurements in exercise physiology; the maximal oxygen uptake. Studies 2, 3 and 4 (Chapters 3, 4 and 5) are field-based studies carried out during regular sport scientific support given to elite and professional athletes.
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We investigated the validity of different lactate and ventilatory threshold methods, to estimate heart rate and power output corresponding with the maximal lactate steady-state (MLSS) in elite cyclists. Elite cyclists (n = 21; 21 +/- 0.4 y; VO2peak, 5.4 +/- 0.2 l x min (-1)) performed either one (n = 10) or two (n = 11) maximal graded exercise tests, as well as two to three 30-min constant-load tests to determine MLSS, on their personal race bicycle which was mounted on an ergometer. Initial workload for the graded tests was 100 Watt and was increased by either 5 % of body mass (in Watt) with every 30 s (T30 s), or 60 % of body mass (in Watt) with every 6 min (T6min). MLSS was defined as the highest constant workload during which lactate increased no more than 1 mmol x l (-1) from min 10 to 30. In T30 s and T6 min the 4 mmol (TH-La4), the Conconi (TH-Con) and dmax (TH-Dm) lactate threshold were determined. The dmax lactate threshold was defined as the point that yields the maximal distance from the lactate curve to the line formed by the lowest and highest lactate values of the curve. In T30 s also ventilatory (TH-Ve) and Vslope (TH-Vs) thresholds were calculated. Time to exhaustion was 36 +/- 1 min for T30 s versus 39 +/- 1 min for T6 min. None of the threshold measures in T30 s, except TH-Vs (r2 = 0.77 for heart rate) correlated with either MLSS heart rate or power output. During T6 min, power output at TH-Dm was closely correlated with MLSS power (r2=0.72). Low correlations were found between MLSS heart rate and heart rate measured at TH-Dm (r2=0.46) and TH-La4 (r2=0.25), respectively, during T6 min. It is concluded that it is not possible to precisely predict heart rate or power output corresponding with MLSS in elite cyclists, from a single graded exercise test causing exhaustion within 35-40 min. The validity of MLSS predicted from an incremental test must be verified by a 30-min constant-load test.
Article
In 1976, the aerobic-anaerobic threshold first defined a point on the lactate power curve as a) a transition from aerobic to partly anaerobic energy metabolism, b) an indicator of aerobic performance and c) a means of predicting exercise intensities in endurance training. Subsequent threshold concepts detected selected changes of the lactate power curve either based upon theories concerning lactate formation and utilisation or on empirical training observations. Studies on the highest steady state of lactate during prolonged constant workload, termed maxLass or MLSS, support the idea of a transition from aerobic to partly anaerobic energy metabolism. The shape of the lactate power curve and corresponding thresholds are testing-protocol dependent. Thresholds as well as performances at other arbitrarily-selected points on the lactate power curve correlate comparably well with MLSS power and maximum endurance performance. There is no consistent theory or experimental evidence for an interrelationship between MLSS, MLSS exercise intensity and aerobic performance, nor that thresholds indicate superior training intensities. Mechanisms linking lactate concentrations with specific training effects remain unclear. There is no need for new lactate threshold concepts. Existing data should be used to identify reference values in order to adjust lactate performance testing to biomedical standards. Prospective and observatory training studies should advance knowledge regarding the meaning of specific lactate concentrations for achieving defined training effects. Computer modelling appears to be underused in developing and testing hypotheses on complex effects related to new findings at (sub-)cellular level and their meaning for exercise testing, lactate power curve and training.
Article
30-min constant load tests are known as the gold-standard for measuring the maximum-lactate-steady-state (maxLass). There is a lack of studies investigating the reliability of this method. The aim of this investigation was the determination of reliability of power (PmaxLass) and lactate concentration (LamaxLass) at maxLass during a 30-min constant load cycle ergometry test. METHOD: 9 male subjects (25±4 years, 181±7cm, 77±8 kg) underwent several 30-min-constant load tests. During this time, blood-samples were taken from the earlobe after 4,8,10,14,18,22,26 und 30 minutes for detecting the LamaxLass. A maxLass was defined as the highest workload that could be maintained without an increase in lactate concentration of more than 0,05 mmol/l/min. The individual's maxLass was determined 4 times. RESULTS: Mean of PmaxLass was 207 Watt ±29 Watt and mean of LamaxLass 6,2 mmol/l ±1,62 mmol/l. The coefficient of variability could be calculated for PmaxLass with 3,8% and 15% for LamaxLass. For the determination of reliability the Intra-Class-Coefficient (ICC) was calculated for PmaxLass with r=0,92 (p≤0,001) and r=0,66, (p<=0,001) for LamaxLass. DISCUSSION: PmaxLass in constant load tests is highly reliable and therefore comparable with results of incremental tests. The high variability of LamaxLass shows that a determination of endurance-performance based on lactate concentration should be interpreted carefully.
Article
During the last nearly 50 years, the blood lactate curve and lactate thresholds (LTs) have become important in the diagnosis of endurance performance. An intense and ongoing debate emerged, which was mainly based on terminology and/or the physiological background of LT concepts. The present review aims at evaluating LTs with regard to their validity in assessing endurance capacity. Additionally, LT concepts shall be integrated within the ‘aerobic-anaerobic transition’ — a framework which has often been used for performance diagnosis and intensity prescriptions in endurance sports. Usually, graded incremental exercise tests, eliciting an exponential rise in blood lactate concentrations (bLa), are used to arrive at lactate curves. A shift of such lactate curves indicates changes in endurance capacity. This very global approach, however, is hindered by several factors that may influence overall lactate levels. In addition, the exclusive use of the entire curve leads to some uncertainty as to the magnitude of endurance gains, which cannot be precisely estimated. This deficiency might be eliminated by the use of LTs. The aerobic-anaerobic transition may serve as a basis for individually assessing endurance performance as well as for prescribing intensities in endurance training. Additionally, several LT approaches may be integrated in this framework. This model consists of two typical breakpoints that are passed during incremental exercise: the intensity at which bLa begin to rise above baseline levels and the highest intensity at which lactate production and elimination are in equilibrium (maximal lactate steady state [MLSS]). Within this review, LTs are considered valid performance indicators when there are strong linear correlations with (simulated) endurance performance. In addition, a close relationship between LT and MLSS indicates validity regarding the prescription of training intensities. A total of 25 different LT concepts were located. All concepts were divided into three categories. Several authors use fixed bLa during incremental exercise to assess endurance performance (category 1). Other LT concepts aim at detecting the first rise in bLa above baseline levels (category 2). The third category consists of threshold concepts that aim at detecting either the MLSS or a rapid/distinct change in the inclination of the blood lactate curve (category 3). Thirty-two studies evaluated the relationship of LTs with performance in (partly simulated) endurance events. The overwhelming majority of those studies reported strong linear correlations, particularly for running events, suggesting a high percentage of common variance between LT and endurance performance. In addition, there is evidence that some LTs can estimate the MLSS. However, from a practical and statistical point of view it would be of interest to know the variability of individual differences between the respective threshold and the MLSS, which is rarely reported. Although there has been frequent and controversial debate on the LT phenomenon during the last three decades, many scientific studies have dealt with LT concepts, their value in assessing endurance performance or in prescribing exercise intensities in endurance training. The presented framework may help to clarify some aspects of the controversy and may give a rationale for performance diagnosis and training prescription in future research as well as in sports practice.
Article
Anaerobic threshold as a basic criterion of training recommendation can be estimated by various parameters. The purpose of this study was to investigate the relationship and the reproducibility of ventilatory, lactate-derived and catecholamine thresholds of an incremental treadmill exercise. Therefore, 11 male subjects underwent two incremental treadmill tests within 7 days. The lactate threshold (LT) was determined at the lowest Value of the lactate-equivalent (ratio lactate/performance). The individual anaerobic threshold (IAT) was calculated at LT + 1.5 mmol/L lactate. The ventilatory thresholds, using mass-spectrometry, were defined by the V-slope method (AT) and at the deflection point of end-tidal CO2 (ET-CO2) concentration (RCP). The thresholds of epinephrine (TE) and norepinephrine (TNE) were calculated in the manner of LT. The running velocities were highly reproducible at LT (test-retest correlation coefficient r = 0.90), IAT (r = 0.97), AT (r = 0.88) and RCP (r = 0.95). By contrast TE (r = 0.49) and TNE (r = 0.46) showed a poor reproducibility. TE and TNE occurred 5 - 11 % below LT and AT with a low correlation to LT and AT. LT was found 4 % below AT, both were correlated with r = 0.70 (p < 0.01, test 1) and r = 0.95 (p < 0.01, test 2). IAT occurred 7 - 8% above RCP, in both tests a close correlation was found between IAT and RCP of r = 0.97 (p < 0.01). In summary, the ventilatory and lactate-derived thresholds show a high and similar reproducibility, but the catecholamine threshold does not. In the present exercise protocol, there are systematic differences between the lactate-derived and ventilatory thresholds, in spite of a close relationship, and these must be taken into account in recommendations derived for training.
Article
The upper limit of blood lactate resulting in a lactate steady state during prolonged exercise is called the maximal lactate steady state (MLSS). The purpose of this study was to investigate the lactate response to steady-state exercise during a field test in elite endurance athletes. Plasma lactate levels were assessed in 13 elite triathletes and 13 elite cyclists (mean +/- SD; age 23.7 +/- 5.1 yr; HT 180.2 +/- 6.3 cm; WT 70.3 +/- 5.9 kg; VO2 max 68 +/- 3.7 ml/min/kg) during a 40 km-long time trial on a bicycle (4 km course x 10 laps). The steady state was demonstrated by monitoring the heart rate and timing every course run. The lactate levels were expected to correspond to MLSS. The mean level of lactate during the time trial was 7.4 +/- 2.5 mmol/l. Five athletes maintained plasma lactate levels which exceeded 10 mmol/l or more for almost 1 h. The large value of individual variability was conspicuous (range 3.2-12.2 mmol/l). These values exceeded all previous reported levels for MLSS from other investigators. Our observations are important in sport medical practice since the different lactate responses to exercise are used as parameters in training management.
Article
Analyzes were made on muscle samples taken from the lateral part of the m. quadriceps femoris of man (lactate, pyruvate, and pH) on venous blood (lactate, pyruvate) and on capillary blood (pH). Samples were taken at rest, immediately after termination of dynamic exercise and during 20 min recovery from exhaustive dynamic exercise. Muscle pH decreased from 7.08 at rest to 6.60 at exhaustion. Decrease in muscle pH was linearly related to muscle content of lactate + pyruvate. The relationship was slightly different from what has been obtained after isometric exercise and this difference was ascribed to acid-base exchange with the blood during dynamic exercise. Lactate content was highly elevated in muscle after exercise and the concentration was 2–3 times higher than in blood. Pyruvate content was, however, only slightly higher than that at rest. During recovery lactate content of muscle decreased exponentially with respect to time, whereas pyruvate content increased. The half-time of lactate decrease was 9.5 min. From the lactate dehydrogenase equilibrium relative values on NADH/NAD ratio could be calculated. It was found that NADH/NAD was highly increased after exercise and that it had not returned to the basal value after 20 min recovery.
Article
Exercise with stepwise increasing work loads until exhaustion leads to a curvilinear increase of lactate in blood and typical lactate kinetics in the post-exercise period. Lactate kinetics in blood during exercise and recovery results from diffusion along gradients between muscle and blood and simultaneous elimination. Therefore, a general diffusion-elimination model is presented from which maximal rate of elimination (Em), individual anaerobic threshold (IAT), gradient between muscle and blood (deltaC-deltaCEm), muscle volume working above the IAT (Vm), individual membrane constant (Mc), quantity of lactate accounting for lactate gradient (Agrad), and whole body lactate (Anet) can be obtained. For demonstration purpose, this model was applied to a highly trained athlete. In this example, all constants and variables mentioned above as well as an equation reflecting individual lactate kinetics were calculated. Furthermore, the IAT was determined in 61 athletes participating in different events. In general, it can be demonstrated that with increasing aerobic capacity the lactate concentration at the IAT decreases. The lactate concentration at the IAT varies interindividually within broad limits, thus emphasizing the need for individual assessment.
Article
This study was designed to examine the interrelationships among endurance running performance (marathon), the exercise intensity at which the "onset of blood lactate accumulation" (OBLA) occurs training volume, and muscle fiber characteristics. In conjunction with Stockholm's Marathon (1979), 18 male subjects underwent a test to determine the relationship between treadmill running velocity and blood lactate accumulation. The velocity at which a blood lactate accumulation of 4 mmol x l-1 occurred was referred to as the VOBLA. The m. vastus lateralis was biopsied and muscle fiber type distribution (% slow twitch, ST) and capillary density determined. With marathon running velocity (VM) as the dependent variable, multiple regression analysis showed that VOBLA accounted for 92% of the variation in VM, and VOBLA plus training volume prior to the marathon accounted for 96% of this variation. All performance variables were positively correlated to % ST muscle fiber distribution (r = 0.55-0.69) and capillary density (r = 052-0.63). Thus, marathon running performance was closely related to VOBLA and to the ability to run at a pace close to that velocity during the race. These properties were in turn related to % ST, capillary density, and training volume.
Article