Reliability and validity of the 3-min all-out running test

Abstract

Background/Aim

Determine critical speed (CS) and running distance above CS (D’), as estimated from the 3-min all-out running test (3MT) is reliable and predictive of CS and D’ determined from time trials.

Methods

Seven males (26 ± 5 years, VO_2max: 56.6 ± 4.1 ml·kg^-1·min^-1) completed an incremental treadmill test, three separate time trials (T_lim) of 800, 1600, and 2400m to determine CS and D’, and two 3MTs to estimate CS and D’.

Results

Estimates of trial 1 (CS =3.90±0.41 m·s^-1, D’=176±42 m) and trial 2 (CS=3.89 ± 0.48 m·s^-1, D’ = 183±35 m) of the 3MT did not differ. Estimates of CS (ICC=0.95, CV=2.97%) and D’ (ICC=0.93, CV=5.12%) from the 3MT were reliable. The 3MT trials provided valid estimates of CS as determined using regression of the three time trials (ICCs ranged 0.88-0.93, TE ranged 0.13-0.15 m·s^-1, CV ranged 3.32-4.76%). The 3 MT underestimated D’ by∼16%, a difference exceeding the test-retest variability.

Conclusions

Estimates of CS were valid and reliable; however, assessment of D’ from the 3MT may not estimate anaerobic capacity accurately.

KEYWORDS
Anaerobic capacity; Critical speed; Critical power; Fatigue

Resumo

Antecedentes/Objetivo

Determinar se a velocidade crítica (CS) e a distância acima da CS (D’), estimadaso pelo teste de três minutos máximo (3MT) é confiável e preditiva de CS e D’ determinada a partir dos modelos lineares.

Métodos

Sete homens (26 ± 5 anos, VO_2max:56,6 ± 4,1 ml·kg^-1·min^-1) completaram um teste incremental em esteira, três tomadas de tempo separadamente para 800, 1.600 e 2.400 m, para determinar CS e D’ e 2 3MTs para estimar CS e D’.

Resultados

O primeiro 3 MT (CS = 3,90 ± 0,41 m·s^-1, D’ = 176 ± 42m) e o segundo 3 MT (CS = 3,89 ± 0,48 m·s^-1, D’ = 183 ± 35m) não foram diferentes. Estimativas do CS (ICC = 0,95, CV = 2,97%) e D’ (ICC = 0,93, CV = 5,12%) a partir de 3 MT eram confiáveis. Os 3 MTs forneceram estimativas válidas de CS (ICC variou de 0,88-0,93, variaram de 0,13 a 0,15 ms TE, CV variou de 3,32 a 4,76%); 3 MT subestimou D’ em ∼16%, uma diferença maior de variabilidade teste-reteste.

Conclusões

Estimativas CS são válidas e confiáveis. No entanto, a avaliação de D’ a partir do 3MT não pode estimar com precisão a capacidade anaeróbica.

PALAVRAS-CHAVE
Capacidade anaeróbia; Velocidade crítica; Potência crítica; Fadiga

Antecedentes/Objetivos

Determinar si la velocidad crítica (VC) y la distancia por encima de la VC (D’), según la estimación de la prueba de 3 minutos máximos (3MT) es fiable y predictiva de la VC y la D’ determinada a partir de los mejores tiempos de carrera personales.

Métodos

Siete varones (26 ± 5 años, VO_2máx: 56,6 ± 4,1 ml·kg-¹·min^-1) completaron una prueba progresiva en la cinta de correr, con tres períodos de tiempo separados de 800, 1.600 y 2.400 m para determinar la VC y la D’ y dos 3MT para estimar la VC y la D’.

Resultados

Las estimaciones del ensayo 1 (VC = 3,90 ± 0,41 m·s^-1, D’ = 176 ± 42 m) y del ensayo 2 (VC = 3,89 ± 0,48 m·s^-1, D’ = 183 ± 35 m) de la prueba 3MT no fueron diferentes. Las estimaciones de la VC (ICC = 0,95; CV = 2,97%) y D’ (ICC = 0,93; CV = 5,12%) a partir de la prueba 3MT fueron fiables. Las pruebas 3MT proporcionaron estimaciones válidas de la VC, como se determinó mediante regresión de las tres pruebas de tiempo (ICC se clasificaron desde 0,88 hasta 0,93 y la TE osciló entre 0,13 y 0,15 m.s^-1, la CV varió del 3,32 al 4,76%). La D’ 3MT subestimó el ∼16%, una diferencia superior a la variabilidad test-retest.

Conclusiones

Las estimaciones de VC fueron válidas y fiables; Sin embargo, la evaluación de D’ desde la prueba de 3MT no puede estimar con precisión la capacidad anaeróbica.

PALABRAS CLAVE
Capacidad anaeróbica; Velocidad crítica; Potencia crítica; Cansancio

Introduction

Critical power (CP) represents, at least theoretically, the highest power output that can be sustained from an aerobic energy supply, and has been suggested to demarcate the heavy and severe intensity domains (Poole, Ward, Gardner, & Whipp, 1988). In this context, CP represents a good indicator of performance in long-duration events, and is a useful parameter for training prescription (Bosquet, Leger, & Legros, 2002; Vanhatalo, Jones, & Burnley, 2011). The concept of CP was proposed originally for use with small muscle groups (Monod & Scherrer, 1965) but was later adapted for whole body exercises, including running, cycling, and swimming (Ettema, 1966; Hill, 1993).

Linear regression of total work performed during a series of exhaustive exercise bouts relative to time (Monod & Scherrer, 1965; Moritani, Nagata, deVries, & Muro, 1981) can be used to determine CP and the finite work capacity above CP (W’). Whipp et al. (1982) subsequently determined that the linear regression of power output relative to the inverse of time to exhaustion also could determine CP and W’. These linear models are known as the work-time limit (Linear W-T_lim) and power output-inverse time limit (P-T_lim) models, respectively (Gaesser, Carnevale, Garfinkel, Walter, & Womack, 1995). CP is expressed as the slope the linear W-t_lim model, and theoretically, represents the maximum power output that can be maintained for an extended period without exhaustion (Wakayoshi et al., 1992). The parameter W’ is given by the y-intercept of the W-T_lim model, and is defined as the finite energy reserve from the phosphagen pool, anaerobic glycolytic component, and O₂ stores (Poole et al., 1988). Additionally, W’ also may relate to the attainment of a critical concentration of metabolites linked to muscle fatigue (Jones, Wilkerson, DiMenna, Fulford, & Poole, 2008). When applying the concept to running and swimming activities, power is replaced by speed, CP by critical speed (CS) and W’ by maximal capacity to displace the body (D’) (Jones, Vanhatalo, Burnley, Morton, & Poole, 2010).

Traditionally, use of the CP concept required a subject to exercise to exhaustion at several constant power outputs on separate days, and a early debate centered on the number of bouts that should be performed for valid modeling (Hill, 1993). Therefore, in order to reduce the bias in the research outcomes caused by the combination of different mathematical models and predictive trials (amount and duration), a single 3-min all-out test (3MT) was developed to determine CP (Burnley, Doust, & Vanhatalo, 2006) and W’ (Vanhatalo, Doust, & Burnley, 2007). The 3 MT was based on the concept of CP, which implies that at the time point where W’ becomes wholly depleted, the highest achievable power output is CP (Burnley et al., 2006; Coats et al., 2003; Vanhatalo et al., 2007). Namely, if there was a method to completely deplete W’ (e.g. long all-out exercise bout), the remaining power output should equal CP. Indeed, Vanhatalo et al. (2007) observed that end-test power from 3 min of all-out exercise [i.e., average power output over final 30-s (EP)], and the work above EP (WEP) were not significantly different with CP and W’ determined using repeated exhaustive exercise tests, respectively. Estimation of CP from EP was correlated positively and has a low standard error of estimation (SEE), whereas estimation of W’ from WEP had a greater SEE.

The all-out concept has been adapted subsequently for isokinetic knee-extension exercise (Burnley, 2009), rowing (Cheng, Yang, Lin, Lee, & Wang, 2012), and running (Broxterman, Ade, Poole, Harms, & Barstow, 2013; Pettitt, Jamnick, & Clark, 2012). Pettitt et al. (2012) utilized the two-component model equations from the running 3 MT to predict performances of 800, 1600, and 5000 m outdoor races. Runners with higher D’ values in that study ran at faster speeds relative to their CS. Subsequently, Broxterman et al. (2013) compared estimates of CS and D’ from the 3 MT with the “traditional” model using exhaustive treadmill running bouts. The CS and D’ estimates from the 3 MT and those modeled using the speed-inverse t_lim (S-inverse T_lim) model did not differ. When interpreting these results; however, it is worthy to note that estimations of CS between treadmill and track running performances can differ (Kranenburg & Smith, 1996).

The aforementioned studies demonstrated that the 3 MT can be used to determine CS and D’; however, the validation was associated with laboratory tests and no study has reported on the reliability of these variables. In that regard, the objectives of the study were 1) to determine the test-retest reliability of the running 3 MT and 2) to examine if a 3MT could be used to accurately determine CS and D’ derived from track performances.

Methods

Subjects

Seven college students (26 ± 5 years; 172.4 ± 0.05 cm; 75.5 ± 5.6 kg; VO_2max: 56.0 ± 4.2 ml kg^-1·min^-1) volunteered to participate in the study. Subjects were informed of potential risks and discomforts of participation and provided written informed consent. All participants were apparently healthy, nonsmokers, free from injury, not taking any medication, practiced physical running activities at least twice a week and familiarized with track running performances. Furthermore, the subjects were required to rest the day before the test and to have his last light meal 2-h before the test. The study was approved by the local ethics committee and conformed to the Declaration of Helsinki.

Overview of procedures

Each subject visited the laboratory 7 times with at least 48-h separating each visit. All tests were completed within 4 weeks, at the same time of the day (± 2 h), to minimize the effects of biological variation. The tests consisted of: 1) an incremental treadmill exercise test for determination of VO_2max, 2) three track running performances of 800, 1600, and 2400 m to determine CS and D’, and 3) three running 3 MT bouts. The first 3 MT was used as familiarization and excluded from data analysis. The other two tests were used to evaluate the validity and test-retest reliability of CS and D’ estimates from 3 MT. Running performances and 3MT were conducted on a level 200 m synthetic outdoor track, with minimal climate variations between conditions (wind and weather), and in counterbalanced order. All tests were preceded by a warm up consisting of 5 min of self-selected pace running followed by a 5 min rest.

Incremental treadmill exercise test

An incremental treadmill exercise test was performed to determine VO_2max and maximal aerobic speed (S _max ). The initial treadmill speed was set at 2.22 m s^-1 at a 1% grade and was increased by 0.14 m s^-1 per minute until the participant terminated the test owing to volitional exhaustion. Exercising heart rates were monitored continuously using a telemetric heart rate monitor (model T41, Polar Electro Oy, Kempele, Finland), and each subject breathed through a facemask whereby cardiorespiratory data were measured continuously using an open circuit system with breath-by-breath analyzer (Cosmed Quark CPET, Rome, Italy). The gas-analysis system was calibrated before each test using the manufacturer's recommendations. Ear lobe capillary blood samples (25 µl) were taken immediately, 3 and 5 minutes postexercise. The blood samples were analyzed using an automated blood lactate analyzer (YSI 1500 Sport, Yellow Springs Instruments, Yellow Springs, OH, USA), which was calibrated with standards of 5 mmol L^-1 lactate concentrations immediately before each test. VO_2max corresponded to the highest VO₂ average obtained in 15 s, and S _max was calculated as the speed of the last stage fully completed, plus, if necessary, the fraction of time spent multiplied by 0.14 m s^-1 in the stage at which exhaustion occurred. VO_2max was considered to be reached when a leveling off of VO₂ was observed despite increasing running speed (change in VO₂ at VO_2max < 100 ml min^-1) or when two or more of the following criteria were observed: (1) a final respiratory exchange ratio higher than 1:1; (2) visible exhaustion; (3) a heart rate at the end of exercise within 10 bpm of the predicted maximum (220 - age); and (4) a maximal blood lactate concentration higher than 8 mmol L^-1 (Howley, Bassett, & Welch, 1995).

Linear models for estimating CS and D’

To determine the CS and D’ from linear models, subjects performed separate day, randomized time-trials running over three distances of 800, 1600, and 2400 m. The subjects were instructed to give their best effort during trials and were encouraged verbally throughout the test. Two different linear relationships for determining the CS and D’ were used, and are described as follows:

(1) $\mathit{D}=\left(\mathit{CS}\times \mathit{T}\lim \right)+{\mathit{D}}^{\prime }\left(\mathit{Linear}\mathit{D}-{\mathit{T}}_{\lim }\mathrm{mod}\mathit{el}\right)$

where CS is the slope of the relationship and D’ is the y-intercept of the relationship, and

(2) $S=\left({\mathit{D}}^{\prime }/{\mathit{T}}_{\lim }\right)+\mathit{CS}\left(\mathit{Linear}S-\mathit{inverse}{T}_{\lim }\mathrm{mod}\mathit{el}\right)$

where CS is the y-intercept and D’ is the slope of the relationship (Fukuba & Whipp, 1999).

3-min all-out running test

In the 3MT the subjects were instructed to maintain their velocity as fast as possible at all times throughout the test. Verbal encouragement was provided as motivation; however, the subjects were neither informed of the elapsed time nor remaining time to discourage pacing. Split times were acquired manually by two researchers positioned strategically in the center of the track field to provide a full vision and minimize the parallax error of the cones placed at 20-m intervals along the inside lane of the track. Thus, an elapsed time was obtained every 20-m averaging the time recorded by the two researchers. CS and D’ estimated from 3 MT were obtained according to Pettitt et al. (2012), which assumes that a runner expends their finite energy reserve within 2.5-min of all-out effort, whereby the speed between 150-s and 180-s would be performed in a steady-state. Thus, CS was determined by averaging the speed between 150 and 180 s (m·s^-1), and D’ (m) was derived using the follow equation:

(3) ${\mathit{D}}^{\prime }=150\mathit{s}\mathit{x}\left(S_{150\mathit{s}}-\mathrm{CS}\right)$

where S _150s is the mean speed for the initial 150 s of the 3 MT.

Statistical analyses

All data are expressed as mean ± standard deviation (SD) and rejection of the null hypothesis was accepted at a level of p < 0.05. All parameters were tested for normal distribution by the Shapiro-Wilks test for normality before further analysis. The differences between mean values (Linear D-T_lim model, Linear S-inverse T_lim model, and the 3 MTs) were tested using a repeated-measure, one-way analysis of variance (ANOVA) completed with post hoc Bonferroni-adjusted t tests. package Intraclass correlation coefficient were used to determine the relative consistency of the 3 MT. Typical error, coefficient of variation were calculated to evaluate absolute variations between estimated parameters from 3 MT and the linear models. Data analysis was performed using the Statistical Package (version 18.0 for Windows, SPSS Inc., Chicago, IL, USA) and a modified statistical Excel spreadsheet (Hopkins, 2000).

Results

Mean values of S _max and VO_2max obtained from incremental test were 4.36 ± 0.22 m.s^-1 and 56.6 ± 4.1 ml kg^-1·min^-1, respectively. Performance times for the 800, 1600, and 2400 m distances were 158 ±12 s, 364 ± 34 s and 599 ± 75 s. Summary statistics of the estimated parameters from the 3 MT trials and the linear models are presented in Table 1. For all subjects, the data obtained from the two linear models displayed with a high goodness of fit (R² of 0.955 ± 0.045). The standard errors as a percentage of the parameter estimates of CS and D’ were 1.0 ± 0.7% and 20.7 ± 9.9%, for the linear D-T_lim model and 3.3 ± 2.4% and 13.9 ± 7.4% for the S-inverse T_lim model, respectively.

Table 1
Comparisons (Mean ± SD) for critical speed (CS) and distance above CS (D') across measurements.

Measurement	CS (m·s-1)	D' (m)
Linear D-Tlim Model	3.69 ± 0.50a	239 ± 62
Linear S-Inverse TLim Model	3.77 ± 0.47	212 ± 47
3-min all-out exercise test (Trial 1)	3.90 ± 0.41	176 ± 42b
3-min all-out exercise test (Trial 2)	3.89 ± 0.48	183 ± 35c

• ^a
  Significantly (p <0.05) lower CS measure in comparison to the linear S-T_lim model.
• ^b
  Significantly (p <0.05) lower D' measure in comparison to both the linear D-T_lim model and the linear speed-inverse T_lim model.
• ^c
  Significantly (p <0.05) lower D' measure in comparison to the linear distance-T_lim model.

Figure 1 depicts the determination of CS and D’ from two trials of the 3 MT (left panel) along with linear D-T_lim model (top right panel), and the speed-inverse T_lim model (bottom right panel) for representative subject. Test retest reliability of the CS and D’ estimates were strong (Table 2).

Figure 1
Parameter estimations for a representative subject. A) Data points are 20 m average speeds of the first (o) and (-) second 3-min all-out test (3 MT). The dotted line represents the critical speed (CS) of first and second 3MT, respectively. Distance performed above CS (D’) was determined by the equation: D’ = 150 x (mean speed of the first 150-s - CS), where mean speed of the first 150-s and CS are expressed in m·s^-1 and D’ are expressed in m. B) linear D-T_lim model and C) linear S-inverse T_lim model determined from 800, 1600 and 2400 m running performances. In B) D’ is the y-intercept and CS is the slope of linear regression, whereas in C) D’ and CS is the slope and y-intercept, respectively.

Table 2
Consistency statistics between the 3-min all-out running test (3 MT) trials and comparison with the linear speed-inverse T_lim and linear distance-T_lim models.

Comparisons	Intraclass Correlation Coefficient (α)	Typical Error	Coefficient of Variation
3 MT trial 1 vs. 3 MT trial 2	CS = 0.95 D' = 0.96	CS = 0.10 m s-1 D' = 7.99 m	CS = 2.97% D' = 5.12%
3 MT trial 1 vs. speed-inverse Tlim model	CS = 0.92 D' = 0.93	CS = 0.13 m s-1 D' = 12.08 m	CS = 3.65% D' = 7.18%
3 MT trial 2 vs. speed-inverse Tlim model	CS = 0.93 D' = 0.83	CS = 0.13 m s-1 D' = 17.07 m	CS = 3.32% D' = 9.77%
3 MT trial 1 vs. linear distance-time model	CS = 0.88 D' = 0.93	CS = 0.16 m s-1 D' = 26.91 m	CS = 4.76% D' = 11.39%
3 MT trial 2 vs. linear distance-time model	CS = 0.92 D' = 0.71	CS = 0.14 m s-1 D' = 7.99 m	CS = 3.79% D' = 5.12%

Analysis of CS and D’ revealed a significant main effect (F = 6.925, P=0.045 and F = 10.48, P=0.023, respectively). CS differed between the linear D-T_lim and S-inverse T_lim models (P=0.031), but no differences for CS were observed between the 3 MT trials, or between 3 MTs and the linear models (P>0.2) (Table 1). D’ for the linear models did not differ (P=0.058), but they were significantly higher than D’ estimates from the 3 MT (P<0.05). Small and moderate variations were observed for CS and D’, respectively, between the 3 MT and linear models (Table 2). Moreover, CS and D’ presented high and significant (P<0.01) intraclass correlation coefficients between 3MT and linear methods.

Discussion

To our knowledge, this is the first study that determined the reliability of the running 3 MT, as well its validity for estimating the parameters of linear relationships from track running performances. The high test-retest reliability observed in the present study demonstrates that the parameters estimated from the running 3MT have minimal variability. Furthermore, our results also indicate that the 3 MT is able to estimate CS, however, underestimates D’. Taken together, our results suggest that 3 MT might be useful tool to assess and monitor training-induced changes in aerobic power, but caution is warranted when using 3 MT for measuring anaerobic capacity through the estimate of D’.

The 3 MT for cycling has been reported to have good predictive validity and reliability for CP, and moderate reliability for W’ (Burnley et al., 2006; Johnson, Sexton, Placek, Murray, & Pettitt, 2011; Vanhatalo et al., 2007; Simpson et. al., 2015) when performed under linear factor resistance applied. Nonetheless, the validity is doubtful, when the resistance model was changed (e.g. isokinetic mode) (Karsten B et al., 2014). For rowing, Cheng et al. (2012) reported that the 3 MT provided moderate test-retest reliability for assessing CP (ICC = 0.788) and W’ (ICC = 0.628). In running, our results also demonstrate that 3 MT is sufficiently robust and not significantly affected by biological and technical variations of the test, since CS and D’ estimated from running 3 MT was reliable. These results are impactful because good reliability implies good precision of single measurements, and better tracking of changes in measurements in research or practical settings.

The CS metric has been described as a good indicator of performance in long-duration events, and a useful parameter for training prescription (Bosquet et al., 2002; Vanhatalo et al., 2011); however, the traditional linear models were time-consuming, and might therefore be a problem for experimental studies and training programs. The 3MT has emerged as a solution to this problem (Broxterman et al., 2013; Cheng et al., 2012; Vanhatalo et al., 2007). For running, Broxterman et al. (2013) reported that the CS determined from the 3 MT was positively correlated (r = 0.92) and not significantly different from the CS determined from three exhaustive treadmill running bouts using the S-inverse T_lim model. Further, they observed steady state VO₂ during a subsequent square-wave bout below CS, whereas VO₂ was driven to VO_2max with exhaustion occurring prior to 15 min in a square-wave bout exceeding CS, as determined from the 3 MT. We used track running performances to determine CS and our results are similar to those of Broxterman, et. al. (2013). Moreover, we observed lower typical errors and coefficient of variations between CS estimated from 3 MT and linear models. Collectively, these studies indicated that the running 3 MT may reduce the bias in the research outcomes caused by different mathematical models, and can be applied successfully to determine the CS in both laboratory and field settings.

A finite energy reserve from anaerobic sources and myoglobin O₂ stores (Miura, Sato, Whipp, & Fukuba, 2000; Monod & Scherrer, 1965; Moritani et al., 1981), and/or the attainment of a critical concentration of one or more metabolites linked to muscle fatigue (Jones et al., 2008), are suggested as mediating factors for W’ and D’. In cycling exercise, Vanhatalo et al. (2007) reported that the W’ from 3 MT was predictive of W’ from the linear models; where others have reported lower reliability for W’ (Johnson et al., 2011) and lower predictive validity W’ and D’ (Broxterman et al., 2013; Cheng et al., 2012). In our study, D’ from the 3 MT correlated positively with D’ estimated from the linear models, yet yielded lower estimates. Similarly, Broxterman et al. (2013) reported that the 3 MT underestimated D’ for five of the seven subjects measured, despite not being statistically different. In addition, Galbraith A. et al., 2014 found higher D’ value in the treadmill protocol compared to the field protocols. Differences in energetic demand between methods may account for the discrepancy between parameters. The high speeds sustained in sprint running, cost of acceleration and aerodynamic resistance are important components of the total energy demand during all-out bouts, which is reduced during middle and long performances relying on the linear models, though still higher than treadmill running (di Prampero, Atchou, Bruckner, & Moia, 1986; di Prampero et al., 1993). In this context, Arsac and Locatelli (2002) reported that the energy required to overcome aerodynamic resistance and changes in kinetic energy were 13% and 24% of the total energy demand of 100 m running performance, respectively. Therefore, D’ might have been lower due to the higher energy required by non-locomotor elements during the 3 MT.

A few studies have shown correlations between W’ and D’ with metabolic measures of anaerobic capacity (i.e. maximally accumulated oxygen deficit and work performed in a 30-s all-out cycle ergometer test (Chatagnon, Pouilly, Thomas, & Busso, 2005; Hill & Smith, 1993; Jenkins & Quigley, 1991). However, poor test-retest reliability of W’ and D’ has been observed (Gaesser & Wilson, 1988; Johnson et al., 2011; Taylor & Batterham, 2002) which may complicate its validity to determine anaerobic capacity. Conversely, D’ estimates in our study had good reliability, probably due to the lack of influence of the high variability of time to exhaustion at a given intensity or performance time inherent in the traditional CS model (Whipp & Ward, 2009). Additional studies are needed to analyze the relationships between D’ estimated from different methods and measures of anaerobic capacities to determine the validity and physiological meaning of D’ from 3 MT.

Our study was unique to prior studies in that we determined time and speed of the 3 MT manually every 20-m, whereas Broxterman et al. (2013) and Pettitt et al. (2012) utilized accelerometer strapped to the right foot and GPS systems to record their data, respectively. Manual timekeeping appears to represent an inexpensive and reliable alternative for estimating CS and D’ from the 3 MT.

In summary, the results our study indicates that estimated parameters from 3 MT have good test-retest reliability. Furthermore, despite that 3 MT underestimated D’, CS was similar between estimates from the 3 MT and traditional linear models. Therefore, we conclude a running 3 MT utilizing manual timekeeping is inexpensive and noninvasive method to evaluate aerobic fitness in running; however, further studies are needed for analyze the validity of D’ from 3 MT as a measure of anaerobic capacity.

Pratical applications

Based on the present study, we suggest that the CS from 3 MT is a valid and reliable means to determine aerobic capacity in running. Therefore, a running 3 MT utilizing manual timekeeping can be used as an inexpensive, noninvasive and suitable method for prescribing aerobic training and monitoring training-induced performance changes in aerobic capacity; however, we recommend caution when using D’ from 3 MT as a measure of anaerobic capacity.

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Publication Dates

• Publication in this collection
  Jul-Sep 2018

History

• Received
  12 Aug 2015
• Accepted
  20 Feb 2018