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Software for calculating blood lactate endurance markers

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Journal of Sports Sciences
Authors:
John Newell at Ollscoil na Gaillimhe – University of Galway
John Newell
David Higgins at Berlin Institute of Health
David Higgins
  • Berlin Institute of Health
Niall Madden at Ollscoil na Gaillimhe – University of Galway
Niall Madden
James Cruickshank at Ollscoil na Gaillimhe – University of Galway
James Cruickshank
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Abstract and Figures

Blood lactate markers are used as summary measures of the underlying model of an athlete's blood lactate response to increasing work rate. Exercise physiologists use these endurance markers, typically corresponding to a work rate in the region of high curvature in the lactate curve, to predict and compare endurance ability. A short theoretical background of the commonly used markers is given and algorithms provided for their calculation. To date, no free software exists that allows the sports scientist to calculate these markers. In this paper, software is introduced for precisely this purpose that will calculate a variety of lactate markers for an individual athlete, an athlete at different instants (e.g. across a season), and simultaneously for a squad.
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Software for Calculating Blood Lactate Endurance Markers
J. Newell
1
, D. Higgins
1
, N. Madden
1
, J. Cruickshank
1
, J. Einbeck
1
.
1
Department of Mathematics, National University of Ireland, Galway;
Address for Correspondence:
J. Newell, PhD
Department of Mathematics,
National University of Ireland, Galway,
Galway, Ireland
Phone: + 353 91 493703
Fax: + 353 91 750542
Email: John.Newell@Nuigalway.ie
1
Abstract
Blood lactate markers are used as summary measures of the underlying model of lactate
production in athletes. Several markers have appeared in the literature in the last 20 years
and papers comparing their performance (i.e. relation to endurance) presented.
A typical lactate curve exhibits a curvilinear pattern but there is no consensus on the true
model of lactate production. The main debate concerns whether a lactate curve is
comprised of two sections – a linear baseline and a region of rapidly increase lactate
production. The intersection of these two sections is thought to represent a breakpoint, or
threshold [1,2,3,4,5]. The alternative suggestion is that a lactate curve is simply a smooth
monotonicly increasing curve [6]. Given this debate several markers have been
suggested. Typically each marker corresponds to a workload in the region of high
curvature in the lactate curve.
To date no free software exists that allows the sports scientist to calculate these markers
in a consistent manner. In this paper software is introduced for precisely this purpose.
The software will calculate a variety of lactate markers for an individual player, a player
across different time points (e.g. across a season) and simultaneously for a team.
A concise description of the markers considered is given in addition to the algorithms
used to calculate the markers.
Keywords: Lactate Curves, Endurance Markers, Software.
2
Introduction
The main controversy surrounding blood lactate analysis is whether there is a breakpoint
(i.e. threshold) present in the lactate curve or whether lactate increases as a monotonically
increasing smooth function. Note the presence of a breakpoint implies a discontinuity in
the first derivative of the lactate curve. This assumption does not imply that the lactate
curve itself is non continuous, as highlighted by Morton [7].
The breakpoint is considered to represent a workload where lactate production and
clearance are equal and is defined as the Lactate Threshold (LT). Improved endurance is
associated with prolonging the LT. This proposed change point is thought to represent a
switch from one physiological system to another which has been debated in the literature
[8,9,10,11,12,13,14,15,16].
The work from Hughson [6] however suggests that during incremental exercise the
change in blood lactate is a continuum that does not display a threshold phenomenon. In
light of this additional markers have been proposed.
Lactate Markers
1. Assuming a breakpoint exists
If it is assumed that the lactate threshold exists (i.e. a unique point where lactate
production switches from an anaerobic to aerobic state) two objective approaches have
been suggested to determine this breakpoint.
3
Traditionally the LT was determined subjectively from plots of the lactate concentration
versus workload by identifying the treadmill velocity or workload that best corresponds
to a departure from a linear baseline pattern. Lundberg et al. [17] proposed fitting a
linear spline where the estimated workload corresponding to the location of the knot is
the LT. The location of the knot (i.e. the point of intersection between the two linear
splines) and the parameters of the lines are estimated by minimizing the sum of the
squared differences between the observed lactate values and the fitted values.
Under this model it is assumed that the relationship between blood lactate L and
workload w for individual i is given by
101 2
=()
iijij
LwwLT,
ij
β
ββ ε
+
++ − +
(1.1)
for i=1,…,N individuals, j=1,…,n
i
workloads
where the error term ε is assumed independently distributed with mean zero and finite
variance. Note that the notation (…)
+
means the positive part of the argument. This
model is an example of a broken stick regression model, with the ‘break’ occurring at the
lactate threshold LT. The value of LT can be estimated using simple linear regression by
fitting model 1.1 and identifying the workload LT, corresponding to the model with
minimum Mean Squared Error.
4
A log transformation of both the workload and blood lactate concentration has been
suggested (LT
loglog
) in an attempt to gain a better estimate of the lactate threshold [1].
Criticisms of the LT marker include that it may be estimating a feature that does not
actually exist; it is using linear regression which is quite sensitive to outliers in small data
sets (i.e. there can be a considerable difference in the estimate of the LT following small
changes in the recorded lactate); it may not be appropriate to use linear splines if the
increase in lactate post LT is curvilinear.
Criticisms of the LT
loglog
are similar to those highlighted for the LT marker but also
include the fact that taking logarithms of both the lactate and workload in some way
assumes that the increase in lactate post LT is exponential. Thus assumption may be
difficult to justify as the use of an exponential function in the model suggests that
somehow the rate of change of lactate depends on the amount of lactate.
2. Assuming a breakpoint does not exists
Several additional lactate markers have been suggested if it is assumed that the lactate
curve is a smooth process. There is a tendency in the literature [18] to refer to these
markers incorrectly as lactate thresholds rather than endurance markers. The Lactate
Threshold is a particular marker referring to the presence of a breakpoint while an
endurance marker is a general term used to represent any single summary statistics
derived from a sample of blood lactate data.
5
The markers in this section typically have no physiological interpretation but appear to
estimate workloads corresponding to points of curvature on the lactate curve.
2.1 DMax
The workload corresponding to the point that yields the maximum perpendicular from a
line L
2
, joining the first and last lactate measurements to the estimated lactate curve L
3
[2].
The line joining the first and last lactate measurements can be estimated using simple
linear regression
2
L
201
=
iij
Lw
ij
β
βε
+
+
(2.1)
for i=1,…,N individuals, j=1, n
i
workloads only
An estimate of the true lactate curve is calculated by fitting a polynomial regression
model (typically of degree 3)
23
301 2 3
=
iijijij
Lwww
ij
β
ββ β ε
+
+++
(2.2)
for i=1,…,N individuals, j=1,…,n
i
workloads
The point of maximum perpendicular distance from and corresponds to the
workload w
2
L
3
L
DMax
where
3
2
=
dL
dL
dw dw
(i.e. the workload where the first derivative of and are equal).
2
L
3
L
6
The main criticism of the DMax marker is its dependence on both the initial and final
lactate reading. The initial and final workloads where the lactate data are collected will
have a direct influence on the value of this marker. If it is assumed that there is no
breakpoint then the location of the DMax will depend on the arbitrary choices of the first
and last workloads. If however there was a breakpoint in the lactate curve then the DMax
would to be less sensitive to these workloads.
2.2 FBLC
The workload corresponding to a fixed blood lactate concentration (FBLC), typically
4mmol [4]. This is calculated using inverse prediction by finding the workload w such
that the estimated model (2.2)
2
301 2 3
ˆˆ ˆ ˆ
ˆ
=
iijij
Lww
ββ β β
++ +
3
ij
w
= FBLC
The FBLC is a marker that represents a ceiling value for lactate. The main criticism of
the FBLC marker is the considerable variability present at higher workloads [19].
2.3 FRPB
The workload preceding an increase in lactate concentration of a Fixed Rise Post
Baseline (e.g. 1mmol from baseline). Let L
baseline
represent the lactate reading at baseline.
The FRPB marker is calculated by finding the workload w corresponding to a selected
rise from baseline (e.g. 1mmol)
3,
ˆ
- =FRPB
j baseline
LL
The choice of baseline reading is clearly important as is the subjectivity of the choice of
lactate rise.
7
2.4 TEM
The workload preceding an increase in lactate concentration greater than the determined
error of measurement of the lactate analyser. This is calculated by finding the minimum
workload w such that
3, 1 3,
ˆˆ
- >TEM
jj
LL
+
for j=1,…,n-1 workloads.
Limitations of this marker include the measurement error estimate of the machine and the
subjective choice of TEM value.
2.5 D2LMax
The workload corresponding to the point of maximum acceleration of the estimated
underlying lactate curve (i.e. the maximum of the second derivative of the lactate curve).
Smoothing procedures involving polynomial or B-splines are becoming increasing
popular alternatives when interest involves estimating the second derivative of a curve
constructed with no parametric model assumptions.
Assume that the lactate data for the i
th
individual can be modelled as a smooth function L
i
of the workload w
ij
as
,
= ( ) ,
s
mooth ij i ij ij
LLw
ε
+
for i=1,…,N individuals, j=1,…,n
i
workloads
8
It is assumed that L satisfies reasonable continuity conditions on the bounded interval of
interest and that derivatives dL/dw of order 2 can be evaluated. The smoothing procedure
chosen must also take into account that smooth estimates of the first two derivatives of
the lactate curve L
smooth
are required. Penalised smoothing splines [20] using polynomials
of degree 4 are one such choice in order to have a continuous second derivative.
Previous research [20] suggests that choosing the smoothing parameter corresponding to
n-2 df should be adequate.
The workload corresponding to the maximum of the D
2
L
smooth
(w) is calculated as
2
D2LMax= max( ( ))
smooth
DL w .
The main criticism of this marker is the subjectivity on the choice of smoothing
parameter.
A simple approximation of the D2LMax is easily obtained by finite differences of second
order by identifying the corresponding workload to the lactate reading where
discrete 2 1
D2LMax = max( 2 )
ww
LLL
++w
+
for w=1,…,n-2.
It should be noted that the D2LMax
Discrete
will always correspond to a workload where
data were collected.
9
The Software
Code is available to calculate the various markers described above in the form of an
Excel template (Microsoft® Excel 2003) and as a function in R, a freely available
statistics package (http://www.cran.r-project.org). Due to the unavailability of
smooothiing routines in Excel the current version of the template does not calculate the
D2LMax marker while the code provided for R calculates all the markers.
Markers may be calculated for a single athlete (Figures 1 and 2), an athlete across time or
collectively for a squad of players. The team analysis allows the sports scientist to
calculate the various markers for the complete squad in one batch. A dataset with the
results for the complete squad in addition to a report for each player individually is
generated. Interpolated estimates of variables such as VO
2
and Rate of Perceived
Exertion for example at the lactate markers are available also (Figure 1).
The software is available for download at http://www.nuigalway.ie/maths/jn/Lactate.
10
Conclusion
Blood lactate endurance markers are statistics representing unique features of a blood
lactate curve. These markers are typically used to monitor the training status of athletes
and to assist in individualised training programmes. Free software is provided for
objective calculation of several of these markers.
ACKNOWLEDGEMENTS.
The primary author gratefully acknowledges the assistance provided by the National
University of Ireland, Galway Millennium Research fund.
11
References
[1] W. L. Beaver, K. Wasserman and B.J. Whipp. Improved detection of lactate
threshold during exercise using a log–log transformation J. Appl. Physiol.
59
1936–40 (1985).
[2] B. Cheng, H. Kuipers, A.C. Snyder, H.A. Keizer, A. Jeukendrup and M.
Hesselink. A new approach for the determination of ventilatory and lactate
thresholds Int. J. Sports Med.
13 518–22 (1992).
[3] J.A. Davis. Anaerobic threshold: review of the concept and directions for future
research Med. Sci. Sports Exerc.
17 6–18 (1985).
[4] H. Heck, A. Mader, G. Hess, S. M¨ucke, R. M¨uller and W. Hollmann.
Justification of the 4-mmol/L lactate threshold Int. J. Sports Med.
6 117–30
(1985).
[5] H. Stegmann, W. Kindermann and A. Schnabel. Lactate kinetics and individual
anaerobic threshold Int. J. Sports Med.
2 160–5 (1981).
[6] R.L. Hughson, K.H. Weisiger and G.D. Swanson. Blood lactate concentration
increases as a continuous function in progressive exercise J. Appl. Physiol.
62
1975–81 (1987).
[7] R.H. Morton. Detection of a lactate threshold during incremental exercise? J Appl
Physiol 67: 885-888 (1989).
[8] G.S. Anderson and E.C. Rhodes. A review of blood lactate and ventilatory
methods of detecting transition thresholds Sports Med.
8 43–55 (1989).
[9] G.A. Brooks. Anaerobic threshold: review of the concept and directions for future
research Med. Sci. Sports Exerc.
17 22–31(1985).
12
[10] E.F. Coyle. Integration of the physiological factors determining endurance
performance ability Exerc. Sport Sci. Rev.
23 25–63 (1995).
[11] J.A. Davis, P. Vodak, J.H. Wilmore, J. Vodak and A. Kurtz. Anaerobic threshold
and maximal aerobic power for three modes of exercise J. Appl. Physiol.
41 544–
50 (1976).
[12] C.E. Loat and E.C. Rhodes. Relationship between the lactate and ventilatory
thresholds during prolonged exercise Sports Med.
15 104–15 (1993).
[13] T.M. McLellan. Ventilatory and plasma lactate response with different exercise
protocols: a comparison of methods Int. J. Sport Med.
6 30–5 (1985).
[14] K. Meyer, R. Hajric, S. Westbrook, L. Samek, M. Lehmann, M. Schwaibold, P.
Betz and H. Roskamm. Ventilatory and lactate threshold determined in healthy
normals and cardiac patients: methodological problems Eur. J. Appl. Physiol. 72
387–93 (1996.
[15] M.L. Walsh and E.W. Banister. Possible mechanisms of the anaerobic threshold.
A review Sports Med.
5 269–302 (1988).
[16] K. Wasserman, B.J. Whipp, S.N. Koyal and W.L. Beaver. Anaerobic threshold
and respiratory gas exchange during exercise J. Appl. Physiol.
35 236–43 (1973).
[17] M.A. Lundberg, R.L. Hughson, K.H. Weisiger, R.H. Jones and G.D. Swanson.
Computerized estimation of lactate threshold. Comput Biomed Res 19:481-486,
(1986).
[18] T. Noakes. Lore of Running-4th Edition, Human Kinetics, ISBN: 0873229592
(2003).
13
[19] J. Newell, K. McMillan, S. Grant and G. McCabe (2005). Using functional data
analysis to summarise and interpret Lactate curves. To appear in Computers in
Biology and Medicine.
[20] J.O. Ramsay, B.W. Silverman, Functional Data Analysis. Springer, New York,
(1997).
14
Dr. John Newell is a lecturer in Statistics in the Department of Mathematics, National
University of Ireland, Galway. Dr. Newell is a Fulbright Scholar, holds a BSc (Hons.) in
Mathematics (National University of Ireland, Galway) an MSc in Statistics (National
University of Ireland, Cork) and a PhD in Statistics (University of Glasgow, Scotland).
His main area of research involves Applied Statistics including survival analysis,
computational inference, functional data analysis and applications in sports science. He
has co-authored over 25 peer-reviewed publications. He is the consultant statistician for
the Sports Performance Unit, Glasgow Celtic Football Club.
David Higgins is a final year undergraduate student Computer Science in the Department
of Mathematics, National University of Ireland, Galway.
Dr Niall Madden is a lecturer in the Department of Mathematics, National University of
Ireland, Galway. His main research interest is in the numerical analysis of singularly
perturbed differential equations, but also has worked on the modelling of the turbulent
interaction of waves and currents, including solution of differential equations and
generalization of splines for modelling data. He has co-authored 8 peer-reviewed journal
articles, and has an MSc and PhD in Mathematics from University College Cork, Ireland.
Dr. James Cruickshank is a lecturer in the Department of Mathematics, National
University of Ireland, Galway. Dr. Cruickshank holds a B.Sc. and M.Sc. in Mathematics
from the National University of Ireland, Galway and a Ph.D. from the University of
15
Alberta. His research interests lie in any geometrically inspired area of mathematics-
from polyhedra to interpreting geometric features of lactate curves.
Dr. Jochen Einbeck is a Post Doctoral student in the Department of Mathematics,
National University of Ireland, Galway, working for a project funded by the Science
Foundation Ireland (SFI). Dr. Einbeck holds a PhD in Statistics (Ludwig-Maximilians-
University Munich). His has authored and co-authored several recent publications on
nonparametric regression methods in peer-reviewed statistical journals.
16
Figure 1. Sample Excel Output for a Single Athlete.
17
Figure 2. Sample R Output for a Single Athlete
200 250 300 350
Speed (km/h)
0 5 10 15
Lactate mmol
200 250 300 350
Speed (km/h)
0 5 10 15
Lactate mmol
200 250 300 350
Speed (km/h)
0 5 10 15
Lactate mmol
200 250 300 350
Speed (km/h)
0.0000 0.0006 0.0012
D2
Lactate Threshold (LT) Smoothed Lactate Curve
DMax D2LMax
18
Figure 3. Sample Excel Output for a Single Athlete across Time
19
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  • Feb 1992
  • Eur J Appl Physiol Occup Physiol
The purpose of this study was to investigate the relationship between threshold points for heart rate (THfc{\text{TH}}_{f_c } ) and blood lactate (Th1a) as determined by two objective mathematical models. The models used were the mono-segmental exponential (EXP) model of Hughson et al. and the log-log (LOG) model of Beaver et al. Inter-correlations of these threshold points and correlations with performance were also studied. Seventeen elite runners (mean, SD = 27.5, 6.5 years; 1.73, 0.05 m; 63.8, 7.3 kg; and maximum oxygen consumption of 67.8, 3.7 ml · kg−1 · min−1) performed two maximal multistage running field tests on a 183.9-m indoor track with inclined turns. The initial speed of 9 km · h−1 (2.5 m · s−1) was increased by 0.5 km · h−1 (0.14 m · s−1) every lap for thef c test and by 1 km · h−1 (0.28 m · s−1) every 4 min for the la test. After fitting the la or thef c data to the two mathematical models, the threshold speed was assessed in the LOG model from the intersection of the two linear segments (LOG-1a; LOG-f c) and in the EXP model from a tangent point (TI-1a; TI-f c). Th1a andTHfc{\text{TH}}_{f_c } speeds computed with the two models were significantly different (P<0.001) and poorly correlated (LOG-1a vs LOG-f c:r=0.36, TI-1a vs TI-f c:r=0.13). In general,THfc{\text{TH}}_{f_c } were less well correlated with performance than Th1a. With two different objective mathematical models, this study has shown significant differences and poor correlations between Th1a andTHfc{\text{TH}}_{f_c } . Thus thef c inflection point with Conconi's protocol is a poor indicator of the la breakpoint with a conventional multistage protocol and a weaker indicator of running performance.
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A Review of Blood Lactate and Ventilatory Methods of Detecting Transition Thresholds
Article
Full-text available
  • Aug 1989
As far back as the 1930s exercise physiologists recognised the existence of critical levels of work intensity above which lactate accumulation increased drastically and energy production was affected. Investigation of these transition points (thresholds) both invasively and non-invasively has led to much recent controversy. Respiratory exchange variables such as Ve, Ve/VO2, VCO2, excess CO2 and blood lactate have been monitored for simple, double and exponential breakaway points to elucidate these critical work intensities. A number of studies have produced high correlations between endurance performance and anaerobic threshold calculations, further demonstrating the potential existence of critical work intensities. Much of the controversy surrounding these phenomena has centered on mechanisms and nomenclature. The term 'anaerobic threshold' has been severely criticised because in addition to the tissues being oxygen insufficient, an imbalance in the energy systems may have resulted. The anaerobic condition or lactate accumulation may be due to changes in lactate production and removal. Muscle fibre type and the fibre type recruitment patterns may also be important factors in threshold transitions. Further examination is made in this review of non-invasive measures for determining transition thresholds and protocols for elucidating the critical points.
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Physiological Testing of the High-Performance Athlete
Book
  • Jan 1991
The purpose of physiological testing (J.D. MacDougall and H.A. Wenger) what do tests measure? (H.J. Green) testing strength and power (D.G. Sale) testing aerobic power (J.S. Thoden) testing anaerobic power and capacity (C. Bouchard, Albert W. Taylor, Jean-Aime Simoneau, and Serge Dulac) Kknanthropometry (WD. Ross and M.J. Marfell-Jones) testing flexibility (C.L. Hubley-Kozey) evaluating the health status of the athlete (R. Backus and D.C. Reid) modelling elite athletic performance (E.W. Banister).
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Possible mechanisms of anaerobic threshold
Article
  • Jun 1988
The anaerobic threshold consists of a lactate threshold and a ventilatory threshold. In some conditions there may actually be 2 ventilatory thresholds. Much of the work detailing the lactate threshold is strongly based on blood lactate concentration. Since, in most cases, blood lactate concentration does not reflect production in active skeletal muscle, inferences about the metabolic state of contracting muscle will not be valid based only on blood lactate concentration measurements. Numerous possible mechanisms may be postulated as generating a lactate threshold. However, it is very difficult to design a study to influence only one variable. One may ask, does reducing F1O2 cause an earlier occurrence of a lactate threshold during progressive exercise by reducing oxygen availability at the mitochondria? By stimulating catecholamine production? By shifting more blood flow away from tissues which remove lactate from the blood? Or by some other mechanism? Processes considered essential to the generation of a lactate threshold include: (a) substrate utilisation in which the ability of contracting muscle cells to oxidise fats reaches maximal power at lactate threshold; and (b) catecholaminergic stimulation, for without the presence of catecholamines it appears a lactate threshold cannot be generated. Other mechanisms discussed which probably enhance the lactate threshold, but are not considered essential initiators are: (a) oxygen limitation; (b) motor unit recruitment order; (c) lactate removal; (d) muscle temperature receptors; (e) metabolic stimulation; and (f) a threshold of lactate efflux. Some mechanisms reviewed which may induce or contribute to a ventilatory threshold are the effects of: (a) the carotid bodies; (b) respiratory mechanics; (c) temperature; and (d) skeletal muscle receptors. It is not yet possible to determine the hierarchy of effects essential for generating a ventilatory threshold. This may indicate that the central nervous system integrates a broad range of input signals in order to generate a non-linear increase in ventilation. Evidence indicates that the occurrence of the lactate threshold and the ventilatory threshold may be dissociated; sometimes the occurrence of the lactate threshold significantly precedes the ventilatory threshold and at other times the ventilatory threshold significantly precedes the lactate threshold. It is concluded that the 2 thresholds are not subserved by the same mechanism.
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Applied Functional Data Analysis
Chapter
  • Jan 2008
Most statistical analyses involve one or more observations taken on each of a number of individuals in a sample, with the aim of making inferences about the general population from which the sample is drawn. In an increasing number of fields, these observations are curves or images. Curves and images are examples of functions, since an observed intensity is available at each point on a line segment, a portion of a plane, or a volume. For this reason, we call observed curves and images ‘functional data,’ and statistical methods for analyzing such data are described by the term ‘functional data analysis.’ It is the smoothness of the processes generating functional data that differentiates this type of data from more classical multivariate observations. This smoothness means that we can work with the information in the derivatives of functions or images. This article includes several illustrative examples.
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The signifiance of the aerobic-anaerobic transition for the determination of work load intensities during endurance training
Article
  • Oct 1979
  • Eur J Appl Physiol Occup Physiol
Anaerobic and aerobic-anaerobic threshold (4 mmol/l lactate), as well as maximal capacity, were determined in seven cross country skiers of national level. All of them ran in a treadmill exercise for at least 30 min at constant heart rates as well as at constant running speed, both as previously determined for the aerobic-anaerobic threshold. During the exercise performed with a constant speed, lactate concentration initially rose to values of nearly 4 mmol/l and then remained essentially constant during the rest of the exercise. Heart rate displayed a slight but permanent increase and was on the average above 170 beats/min. A new arrangement of concepts for the anaerobic and aerobic-anaerobic threshold (as derived from energy metabolism) is suggested, that will make possible the determination of optimal work load intensities during endurance training by regulating heart rate.
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Anaerobic threshold and maximal aerobic power for three modes of exercise
Article
  • Nov 1976
  • J Appl Physiol
Alterations in selected respiratory gas exchange parameters have been proposed as sensitive, noninvasive indices of the onset of metabolic acidosis (anaerobic threshold (AT) during incremental exercise. Our purposes were to investigate the validity and feasibility of AT detection using routine laboratory measures of gas exchange, i.e., nonlinear increases in VE and VCO2 and abrupt increases in FEO2. Additionally, we examined the comparability of the AT and VO2 max among three modes of exercise (arm cranking, leg cycling, and treadmill walk-running) with double determinations obtained from 30 college-age, male volunteer subjects. The AT's for arm cranking, leg cycling, and treadmill walk-running occurred at 46.5 +/- 8.9 (means +/- SD), 63.8 +/- 9.0, and 58.6 +/- 5.8% of VO2 max, respectively. No significant difference was found between the leg exercise modes (cycling and walk-running) for the AT while all pairwise arm versus leg comparisons were significantly different. Using nine additional subjects performing leg cycling tests, a significant correlation of r = 0.95 was found between gas exchange AT measurements (expressed as % VO2 max) and venous blood lactate AT measurements (% VO2 max). We conclude that the gas exchange AT is a valid and valuable indirect method for the detection of the development of lactic acidosis during incremental exercise.
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A New Approach for the Determination of Ventilatory and Lactate Thresholds
Article
  • Nov 1992
In order to determine the ventilatory threshold (VT) and the lactate threshold (LT) in a reliable way, a new method is proposed and compared with conventional methods. The new method consists of calculating the point that yields the maximal distance from a curve representing ventilatory and metabolic variables as a function of oxygen uptake (VO2) to the line formed by the two end points of the curve (Dmax method). Male cyclists (n = 8) performed two incremental exercise tests a week apart. Ventilatory/metabolic variables were measured and blood was sampled for later lactate measurement during each workload and immediately after exercise. No statistical differences were observed in the threshold values (expressed as absolute oxygen uptake; VO2) determined by the Dmax method and the conventional linear regression method (according to O2 equivalent; EqO2) and venous blood at the onset of blood lactate (OBLA), while VT assessed with the conventional linear method (according to the slope of CO2 output; Vslope) yielded significantly lower threshold values. Similar results were obtained from the reproducibility test. Thus, the Dmax method appears to be an objective and reliable method for threshold determination, which can be applied to various ventilatory or metabolic variables yet yield similar results. The results also showed that breathing frequency can be used to determine VT.
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Continuous increase in blood lactate concentration during different ramp exercise protocols
Article
  • Apr 1989
The applicability of a continuous model description of the blood lactate concentration [( La-]) vs. O2 uptake (VO2) relationship was studied in nine healthy male volunteers during three different ramp exercise protocols. The work rate was increased at either 8, 15, or 50 W/min. The continuous model for [La-] = â + b exp(ĉVO2) was compared statistically with a previously proposed log-log transformation model for the [La-] and VO2 variables. It was found that the mean square error was significantly less for the continuous as opposed to the log-log model (P less than 0.01) by analysis of variance pooled across all three ramp slopes. The mean square errors from the individual ramp slopes were also significantly less for the continuous model by paired t test (P less than 0.05). It was observed that the major contributor to the increased error of the log-log model was at VO2's at or above the intersection point (lactate threshold) of the two linear log-transformed segments. The log-log transformation does not appear to relate to any physiological process. The lactate slope index, taken as the point where the slope of the relationship between [La-] and VO2 (i.e., d[La-]/dVO2) equaled 1, occurred at a mean VO2 of 2.25 and 2.37 l/min for the 15- and 8-W/min ramp slopes, respectively, but at 2.76 l/min for the 50-W/min ramp (P less than 0.05). It is concluded that [La-] increases as a continuous function with respect to VO2 across a wide range of ramp work rate slopes.
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