artigo dmax middle age_ijsm.pdf

Physiology & Biochemistry

Machado FA et al. The Dmax is Highly Related to Performance … Int J Sports Med

accepted after revision March 14, 2011

Bibliography DOI http://dx.doi.org/ 10.1055/s-0031-1275671 Published online: 2011 Int J Sports Med © Georg Thieme Verlag KG Stuttgart · New York ISSN 0172-4622

Correspondence Dr. Fabiana Andrade Machado State University of Maring á Department of Physical Education Av. Colombo 5790 87.020-900 Maring á Brazil Tel.: + 55 / 44 / 3011 4315 Fax: + 55 / 44 / 3011 4470 [email protected]

Key words ● ▶ agreement ● ▶ correlation ● ▶ lactate threshold ● ▶ maximal deviation method

The Dmax is Highly Related to Performance in Middle-Aged Females

The Maximal Deviation Method (Dmax) proposed by Cheng et al. [4] is an objective method that can be used to estimate the LT, and it has an advantage in that it considers an individual ’ s blood lactate response during progressive exercise tests to math-ematically determine the point at which lactatemia will occur [25] . Hence, it is not infl uenced by sub-jective judgments which can bias the estimation, and is instead based upon computation of the point on a lactate-intensity regression curve that yields the maximal distance to the straight line connect-ing the fi rst and last points of that curve. Despite the advantages of the Dmax in terms of objectivity and individuality [25] , few studies have reported results in relation to the Dmax and performance. Using three distinct methods, Nichol-son and Sleivert [17] determined the relationship between the LT and 10-km running speed (S 10 km ) using an indoor 400-m track for young female runners. The mean speed for the S 10 km was 11.7 ± 1.4 km.h − 1 , and the LT estimated by the Dmax (LT Dmax ) was 12.0 ± 0.8 km.h − 1 . The Dmax presented the highest correlation with the S 10 km ( r = 0.84; P < 0.001), followed by the LT deter-mined using a fi xed lactate level of 4 mmol.L − 1 (LT 4 ) ( r = 0.57) and the LT determined at the exer-

Introduction ▼ The lactate threshold (LT) has been widely used to predict endurance performance, to prescribe training intensity and to evaluate training eff ects [1, 2, 17, 18] . Currently, several methods exist that can be used to detect the LT, although these methods sometimes produce diff ering estimates on this point [7, 22, 23] . The existing methods include both subjective methods, such as the visual detection of the LT (LT Visual ) [6] , and objec-tive methods, such as methods based on a fi xed value of lactate concentration [13, 15, 21] . The LT Visual is an experience-dependent method with the disadvantage of subjectivity [7] . The disadvantage of the fi xed lactate level method is that it does not take into account the individual and, thus, does not refl ect individual variation in the lactate response [16, 24] . An LT of 4 mmol.L − 1 can be used for most adults, but for some indi-viduals, an LT of 3 or even 5 mmol.L − 1 is a more suitable value with respect to the variation in individual responses [23] . Thus, due to the large variation in the lactate response to exercise, a fi xed lactate level may not be a valid guideline for training prescription [25] .

Authors F. A. Machado 1 , S. M. F. de Moraes 2 , C. S. Peserico 3 , P. V. Mezzaroba 3 , W. P. Higino 4

Affi liations 1 State University of Maring á , Department of Physical Education, Maring á , Brazil 2 State University of Maring á , Department of Physiologic Sciences, Maring á , Brazil 3 State University of Maring á , Laboratory of Eff ort Physiology – Department of Physiologic Sciences, Maring á , Brazil 4 Catholic University Center Salesiano Auxilium, Department of Physical Education, Lins, Brazil

Abstract ▼ The present study examined whether the run-ning speed at the lactate threshold estimated by the maximal deviation method (LT Dmax ) is highly correlated and in agreement with 10-km road race performance (S 10 km ) in middle-aged female runners. Additionally, the LT Dmax was compared with the visual detection of the infl ection point (LT Visual ), the fi xed lactate level of 4 mmol.L − 1 (LT 4 ) and the peak speed (S peak ) in relation to perfor-mance. Sixteen middle-aged, recreational female runners performed a discontinuous, incremental treadmill test. The initial speed was set at 7 km.h − 1 , and this speed was increased every 3 min by

1 km.h − 1 with a 30-s rest between the stages used for earlobe capillary blood sample collection. All of the participants took part in the same local 10-km road race, and S 10 km mean speed was cal-culated. The speeds (mean ± SD) were 10.5 ± 1.0 (S 10 km ), 10.5 ± 1.0 (LT Visual ), 10.9 ± 0.9 (LT Dmax ), 11.4 ± 1.3 (LT 4 ) and 13.5 ± 1.1 km.h − 1 (S peak ). The LT Dmax had the narrowest limits of agreement (0.3 ± 0.4 km.h − 1 ) and was the most highly cor-related with the S 10 km ( r = 0.98), followed by the S peak ( r = 0.95), LT 4 ( r = 0.85) and LT Visual ( r = 0.81). In conclusion, the LT Dmax should be more widely used to estimate long-distance performance and to verify improvements in training.

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cise intensity that preceded consecutive increases in blood lac-tate levels ≥ 1 mmol.L − 1 ( r = 0.52). Bishop et al. [2] examined six commonly used lactate parameters in trained female cyclists. In this study, the mean power output for the 1-h cycling perfor-mance was 183 ± 19 W, and the LT calculated by the Dmax was 178 ± 24 W. Of the six lactate parameters compared, the power output at the LT calculated by the Dmax exhibited the highest correlation with the 1-h endurance performance ( r = 0.84, P < 0.001), followed by the LT at 4 mmol.L − 1 ( r = 0.81, P < 0.001) and the peak power output ( r = 0.81, P < 0.001). Therefore, the calculation of the Dmax has provided promising results related to estimation of performance in young athletes. However, no study has verifi ed this relationship for older run-ners in a road race. The previous studies had also not verifi ed the agreement between the Dmax and performance. We hypothe-sized that the Dmax would also be highly correlated and in agreement with the level of performance in older runners. Thus, the purpose of this investigation was to verify the correlation and the agreement between running speed at the LT estimated with the Dmax and the performance in a 10-km road race in middle-aged, recreational female runners and to compare the obtained results with other performance indices.

Methods ▼ Participants Sixteen recreational, middle-aged female runners aged 35 – 51 years who were training for a local 10-km road race participated in this study. The 10-km performance of the participants was between 9 and 13 km.h − 1 (45 – 65 % of the world record). The descriptive characteristics of the subjects (mean ± SD) were as follows: age, 42.2 ± 7.5 years; height, 1.63 ± 0.03 cm; body mass, 57.3 ± 6.6 kg; body mass index (BMI), 21.6 ± 2.1 kg.m − 2 ; body fat, 20.4 ± 4.1 % and maximal oxygen uptake (VO 2 max), 53.2 ± 8.0 mL.kg − 1 .min − 1 . The training characteristics of the subjects (mean ± SD) were as fol-lows: experience, 3.1 ± 1.9 years; frequency, 2.6 ± 0.5 days.wk − 1 and distance, 24.9 ± 6.0 km.wk − 1 . Written informed consent was obtained from all participants. The experimental protocol was approved by the local ethics committee (#719 / 2010). All research was conducted ethically according to international standards and as required by the International Journal of Sports Medicine [12] .

Incremental exercise test Participants performed a discontinuous incremental exercise test on a motorized treadmill (INBRASPORT ATL, Porto Alegre, Brazil) with the gradient set at 1 % . Participants were instructed to avoid eating food 2 h before the maximal exercise test, to abstain from caff eine and alcohol and to refrain from strenuous exercise 48 h prior to testing. The initial speed was set at 7 km.h − 1 and this speed was increased by 1 km.h − 1 between each of the 3-min successive stages. Each stage was separated by a 30-s period of rest during which an earlobe capillary blood sample (25 μ l) was collected into a glass tube, and, from this sample, blood lactate was determined by electroenzymatic methods (YSI 1 500, Ohio, USA). Each participant was encouraged to give max-imum eff ort until volitional exhaustion.

Calculation of the mean speed at the local 10-km road race All of the participants of this study took part in the same local 10-km road race, which took place within the 1-month period after the laboratory experiments. This local race occurs annually

on paved city streets. The race began at 5:00 p.m. on a 30 ° C, sunny day with a low relative humidity of approximately 40 % . There were three hydration points along the course of the race. All of the participants were encouraged to give their best perfor-mance. Three runners had problems during the race and were not able to fi nish. The race time of the remaining runners was recorded, and their S 10 km from the road race was calculated in km.h − 1 .

Determination of the speed at the LT by the Dmax The LT Dmax was determined for each participant from the blood lactate concentrations and the speed data obtained from the incremental exercise test. Data were fi tted by the exponential plus constant regression curve [14] :

[LA]( s ) = a + ( b .exp( c . s ))

where s is the speed in km.h − 1 , and [LA]( s ) is the blood lactate concentration (mmol.L − 1 ) as a function of speed (km.h − 1 ); a , b and c are the function parameters that were determined by non-linear regression with the Statistical Package for the Social Sci-ences (SPSS) 17.0 software (SPSS Inc., USA). The point on the regression curve that yielded the maximal per-pendicular distance to the straight line connecting the fi rst and last point of this curve was considered to be the speed at the LT as determined by the Dmax ( ● ▶ Fig. 1 ). The maximal perpendicular distance, which represents the LT Dmax , is observed at the point at which the slope of the exponential plus constant curve is equal to the slope of the straight line connecting the fi rst and last point of this curve. Considering that the slope of the curve is obtained by the fi rst derivative of the exponential plus constant equation, the following equation can be used:

LT Dmax = {ln{[exp( c . s f ) – exp( c . s i )] / [( c . s f ) – ( c . s i )]}} / c

where ln is the natural logarithm, c is the parameter of the expo-nential plus constant equation and s i and s f are the initial and fi nal speeds of the incremental exercise test, respectively. The fi nal speed was considered to be that of the last completed stage.

Determination of the relationship between the LT Dmax and the S 10 km In order to verify the relationship between LT Dmax and S 10 km , the correlation and the agreement between LT Dmax and S 10 km were

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Fig. 1 The speed at the LT as determined by the Dmax method (LT Dmax = 11.7 km.h − 1 ).

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calculated. In addition, the LT Dmax was compared with the LT Visual , with the LT 4 determined by linear interpolation and with the speed of the last completed stage of the incremental exercise test (S peak ). The authors subjectively determined the LT Visual as the speed in which the abrupt sustained increase in blood lac-tate concentration occurred [6] . Any disagreement among the authors was resolved by discussion.

Statistical analyses Data are presented as the mean ± SD. These data were analyzed using the SPSS 17.0 software (SPSS Inc., USA). A Shapiro-Wilk test was used to check the normality of the data distribution. The speeds were compared using a one-factor repeated mea-sures ANOVA with a Bonferroni post hoc test. The relationship between the speeds was examined using Pearson ’ s correlation coeffi cient. Bland-Altman analysis [3] was used to calculate the 95 % limits of agreement between the speeds. Statistical signifi -cance was set at P < 0.05.

Results ▼ The peak speed achieved in the incremental exercise test, the mean speed of the 10-km road race and the speeds at the LT Visual , LT Dmax and LT 4 are given in ● ▶ Table 1 . Three participants did not achieve the lactate level of 4 mmol.L − 1 during the incremental

exercise test. The S peak (km.h − 1 ) was signifi cantly higher than the S 10 km , LT Visual , LT Dmax and LT 4 . The LT Dmax was signifi cantly higher than the S 10 km ( P < 0.05), and the LT 4 was signifi cantly higher than the LT Visual ( P < 0.05). The correlation between the LT Dmax and the S 10 km is presented in ● ▶ Fig. 2 . The value of the Pearson ’ s correlation coeffi cient was 0.98 ( P < 0.001), indicating a very high correlation. It can be noted that nearly all of the measures were below the line of equality. ● ▶ Fig. 2 also presents a predictive equation for the indirect determination of the S 10 km (y) from the LT Dmax (x) for the specifi c population of this study (SEE = 0.19 km.h − 1 ). Addi-tionally, the predictive equations for the indirect determination of the S 10 km from the S peak and for the indirect determination of the S peak from the LT Dmax were:

S 10 km (km.h − 1 ) = (0.83 S peak ) – 0.64 ( R 2 = 0.91; SEE = 0.31 km.h − 1 )

S peak (km.h − 1 ) = (1.20 LT Dmax ) + 0.48 ( R 2 = 0.93; SEE = 0.31 km.h − 1 ) ● ▶ Table 2 presents the correlations between the S peak , LT Visual , LT Dmax , LT 4 and S 10 km as well as the analyses of agreement in relation to S 10 km performance. The LT Dmax was the most highly correlated with the S 10 km performance, followed by the S peak ( r = 0.95), which presented a slightly lower correlation, the LT 4 ( r = 0.85) and the LT Visual ( r = 0.81). The LT Dmax was also highly correlated with the S peak ( r = 0.97). The LT Dmax presented the nar-rowest limits of agreement with performance, whereas the LT 4 presented the largest limits. The mean diff erence (bias) between the LT Dmax and the S 10 km was 0.3 km.h − 1 with the 95 % limits of agreement calculated as − 0.1 and 0.7 km.h − 1 , representing a narrow limit of agreement. In contrast, the LT 4 presented large limits of agreement with the S 10 km ( − 0.6 and 2.2 km.h − 1 ) in comparison to the limits of agreement presented between the LT Dmax and the S 10 km . The S peak and the LT Visual presented moder-ate limits of agreement with the S 10 km . ● ▶ Fig. 3 shows the diff erences between the LT Dmax and the S 10 km plotted against the average values (Bland-Altman Plot).

Discussion ▼ The major fi nding of this study was that the running speed esti-mated by the LT Dmax was highly correlated with the performance level in a 10-km road race for middle-aged, recreational female runners, and this relationship presented narrow limits of agree-ment. Additionally, the LT Dmax exhibited a higher correlation and narrower limits of agreement with performance than the other indices that were evaluated, including the LT Visual , the LT 4 and the S peak .

Table 1 Speeds at 10 km road race, lactate thresholds and peak speed.

Speed (km.h − 1 ) Speed ( % S peak )

Variables Mean ± SD Mean ± SD

S peak (n = 13) 13.5 ± 1.1 – S 10 km (n = 13) 10.5 ± 1.0 * 78.3 ± 2.2 LT Visual (n = 13) 10.5 ± 1.0 * 77.8 ± 5.4 LT Dmax (n = 13) 10.9 ± 0.9 * † 80.6 ± 1.8 † LT 4 (n = 10) 11.4 ± 1.3 * # 84.1 ± 5.4 † # SD, standard deviation; % S peak , percentage of peak speed; S peak , peak speed of the incremental exercise test; S 10 km , mean speed at the 10-km road race; LT Visual , lactate threshold determined by visual inspection; LT Dmax , lactate threshold determined by the maximal deviation method; LT 4 , lactate threshold at the fi xed lactate level of 4 mmol.L − 1 ; * P < 0.05 in relation to S peak ; † P < 0.05 in relation to S 10 km ; # P < 0.05 in relation to LT Visual

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99

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Fig. 2 Correlation between the LT Dmax and the S 10 km performance ( r = 0.98; P < 0.001).

Table 2 Correlation and agreement matrix.

LT Dmax S 10 km BIAS ± 1.96 SD

(S 10 km )

S peak (n = 13) 0.97 † 0.95 † 3.1 ± 1.0 km.h − 1 LT Visual (n = 13) 0.74 * 0.81 * 0.1 ± 1.2 km.h − 1 LT Dmax (n = 13) – 0.98 † 0.3 ± 0.4 km.h − 1 LT 4 (n = 10) 0.89 † 0.85 * 0.8 ± 1.4 km.h − 1 SD, standard deviation; LT Dmax , lactate threshold determined by the maximal devia-tion method; S 10 km , mean speed at the 10-km road race; S peak , peak speed of the incremental exercise test; LT Visual , lactate threshold determined by visual inspection; LT 4 , lactate threshold at the fi xed lactate level of 4 mmol.L -1 ; * P < 0.05; † P < 0.001

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Because the LT is used to predict endurance performance, to pre-scribe training intensity and to evaluate training eff ects, its determination is very important for athletes, coaches and researchers. Many studies have reported methods to estimate the LT. Nevertheless, there is not a gold standard method for its evaluation. Some authors have found encouraging results with regard to the Dmax method. Nicholson and Sleivert [17] reported that the LT Dmax presented the highest correlation ( r = 0.84; P < 0.001) with 10-km running speed on an indoor, 400-m track, followed by the LT 4 ( r = 0.57), in eleven, competitive and recrea-tional, young female runners (age, 21 ± 4 years; VO 2 max, 48 ± 3 mL.kg − 1 .min − 1 ). Bishop et al. [2] observed a similar result for female cyclists (age, 29 ± 10 years; VO 2 max, 48 ± 6 mL.kg − 1 .min − 1 ); the LT Dmax presented the highest correlation with 1-h endurance performance ( r = 0.84, P < 0.001), followed by the LT 4 ( r = 0.81, P < 0.001) and peak power output ( r = 0.81, P < 0.001). Our study is in accordance with both of these studies because we found that the LT Dmax presented the highest correlation with the S 10 km performance, which was followed by the S peak , LT 4 and LT Visual . In the study by Nicholson and Sleivert [17] each partici-pant began the incremental test at a running velocity 2 km.h − 1 below their mean 10-km running speed. The speed was increased by 1 km.h − 1 between each of the 5-min successive stages and the stages were separated by a 1-min period of rest. In contrast, the initial speed in the present study was set at 7 km.h − 1 and this speed was increased by 1 km.h − 1 between each of the 3-min suc-cessive stages, followed by a 30-s period of rest. Nevertheless, despite the diff erences between the incremental test protocols, the LT Dmax presented the highest correlation with 10-km run-ning performance in both studies. The very high correlation between the LT Dmax and the S 10 km in the current study occurred despite many factors that could have weakened the correlation, such as diff erent environmental con-ditions and terrain; the road race started at 5:00 p.m. on a 30 ° C, sunny day with very low, 40 % relative humidity on city streets surfaced with asphalt. Farrell et al. [8] also reported a very high correlation between the speed corresponding to the onset of plasma lactate accumulation and the pace of a 9.7-km race ( r = 0.96) in experienced male distance runners. In that study, the subjects reported to the laboratory for eight days to perform a 10-min steady state run for each incremental speed, with the

change in lactate concentration obtained at each speed. The clear disadvantage of that study was the duration of the test, which was similar to the maximal lactate steady state test duration. In agreement with a study by Bishop et al. [2] , in which the Dmax was strongly correlated with peak power output ( r = 0.81; P < 0.001), the S peak was also highly correlated with the LT Dmax . This high correlation between the S peak and the LT Dmax probably occurred because runners that are able to delay an abrupt increase in blood lactate concentration can reach a higher S peak [2] . Additionally, the fi nal stage (speed) of the incremental test is one of the independent variables of the LT Dmax equation. Despite the strong correlation between the LT 4 and the S 10 km , three run-ners did not reach a lactate level of 4 mmol.L − 1 during the incre-mental exercise test, indicating that this concentration is not appropriate for all individuals because it does not take into account the individual variations that occur in each subject [23] . According to Bland and Altman [3] , comparisons between meth-ods should be made not only in terms of correlation, but also in terms of agreement. However, an agreement test was not exe-cuted in the studies by Nicholson and Sleivert [17] and Bishop et al. [2] In our study, the LT Dmax was consistently higher than the S 10 km and exhibited the narrowest limits of agreement; Sim-ilar to the LT Dmax, the S peak was highly correlated with perfor-mance, but presented only moderate limits of agreement. Therefore, the agreement between the S peak and the S 10 km is not as good as the agreement between the LT Dmax and the S 10 km per-formance despite the high correlations between the S peak , the S 10 km and the LT Dmax . Pearson ’ s correlation is not sensitive to systematic error, which is a limitation in the use of Pearson ’ s correlation by itself to compare methods or variables. In con-trast, the analysis of agreement is sensitive to systematic bias and can be used to verify whether two methods or variables can be used interchangeably or whether one variable can predict the second. Considering the poor agreement between the LT 4 and the S 10 km performance and that three females did not reach a lactate level of 4 mmol.L − 1 , the LT 4 does not appear to be an appropriate S 10 km marker for middle-aged females with the performance levels that were assessed in this study. The LT 4 is highly infl uenced by the levels of lactate, which can vary widely and can be induced by many factors, including nutrition, prior stress and muscle fi ber distribution [5, 20] . In addition, the site (e. g., earlobe and fi ngertip) as well as the method (e. g., venous, arterial and capil-lary) of blood sampling may also aff ect the test result [9, 10, 11, 19] . The Dmax is a robust method in which the most important factor is the lactate-intensity curve shape and not the lactate levels themselves. If the lactate-intensity curve is shifted upwards, for example, the LT Dmax intensity is not changed, whereas the LT 4 intensity would be reduced; this characteristic is another advantage of using the Dmax method for evaluation of the LT. The correlation between the LT Visual and the S 10 km was the smallest observed in our study, and the agreement level for this comparison was not good. Although the LT Visual takes the indi-vidual blood lactate response into consideration, which is simi-lar to the LT Dmax method, the subjectivity of the LT Visual method weakened its relationship with the S 10 km performance. The predictive equation for the indirect determination of the S 10 km from the LT Dmax presented a very small error. The SEE for this equation was 39 % smaller than the equation for the indirect determination of the S 10 km from the S peak . Thus, the generated equation for the indirect determination of the S 10 km from the

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Fig. 3 Bland-Altman Plot: agreement between the LT Dmax and the S 10 km performance. The mean diff erence (bias) between the indices was 0.3 km.h − 1 . The 95 % limits of agreement were between − 0.1 and 0.7 km.h − 1 .

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LT Dmax can be used to predict the S 10 km if applied to individuals with characteristics similar to those of the present study. Addi-tionally, alterations on the environmental conditions and race course all characteristics under which the predictive equation was generated, can increase the error of the S 10 km prediction. In conclusion, the LT Dmax was highly correlated with 10-km road race performance in middle-aged, recreational female runners with narrow limits of agreement. Additionally, the LT Dmax pre-sented higher correlation and narrower limits of agreement with performance than the LT Visual , the LT 4 or the S peak . Thus, the LT Dmax was a running pace that could be sustained in long-dis-tance races by a recreational middle-aged female who has a 10-km race pace that is between 9 and 13 km.h − 1 . Therefore, the LT Dmax should be more widely used by coaches and practitioners to estimate long-distance performance and to verify improve-ments in training. Because the results of this study are limited to this specifi c population, further research is warranted to exam-ine the relationship between the LT Dmax and performance in other populations that have diff erent performance levels.

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