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Article

Predicting Maximal Oxygen Uptake Using the 3-Minute All-Out Test in High-Intensity Functional Training Athletes

by
Joshua D. Dexheimer
1,*,
Shane J. Brinson
2,
Robert W. Pettitt
3,
E. Todd Schroeder
4,
Brandon J. Sawyer
2 and
Edward Jo
5
1
Department of Kinesiology, Azusa Pacific University, Azusa, CA 91702, USA
2
Department of Kinesiology & Biology, Point Loma Nazarene University, San Diego, CA 92106, USA
3
Department of Health Science, Rocky Mountain University of Health Professions, Provo, UT 84606, USA
4
Division of Biokinesiology & Physical Therapy, University of Southern California, Los Angeles, CA 90033, USA
5
Human Performance Research Laboratory, Department of Kinesiology and Health Promotion, California State University Pomona, Pomona, CA 91768, USA
*
Author to whom correspondence should be addressed.
Sports 2020, 8(12), 155; https://doi.org/10.3390/sports8120155
Submission received: 22 October 2020 / Revised: 26 November 2020 / Accepted: 27 November 2020 / Published: 30 November 2020
(This article belongs to the Special Issue Exercise Physiology: New Frontiers for Exercise Testing and Prescription)
Browse Figure
Versions Notes

Abstract

:
Maximal oxygen uptake (VO2max) and critical speed (CS) are key fatigue-related measurements that demonstrate a relationship to one another and are indicative of athletic endurance performance. This is especially true for those that participate in competitive fitness events. However, the accessibility to a metabolic analyzer to accurately measure VO2max is expensive and time intensive, whereas CS may be measured in the field using a 3 min all-out test (3MT). Therefore, the purpose of this study was to examine the relationship between VO2max and CS in high-intensity functional training (HIFT) athletes. Twenty-five male and female (age: 27.6 ± 4.5 years; height: 174.5 ± 18.3 cm; weight: 77.4 ± 14.8 kg; body fat: 15.7 ± 6.5%) HIFT athletes performed a 3MT as well as a graded exercise test with 48 h between measurements. True VO2max was determined using a square-wave supramaximal verification phase and CS was measured as the average speed of the last 30 s of the 3MT. A statistically significant and positive correlation was observed between relative VO2max and CS values (r = 0.819, p < 0.001). Based on the significant correlation, a linear regression analysis was completed, including sex, in order to develop a VO2max prediction equation (VO2max (mL/kg/min) = 8.449(CS) + 4.387(F = 0, M = 1) + 14.683; standard error of the estimate = 3.34 mL/kg/min). Observed (47.71 ± 6.54 mL/kg/min) and predicted (47.71 ± 5.7 mL/kg/min) VO2max values were compared using a dependent t-test and no significant difference was displayed between the observed and predicted values (p = 1.000). The typical error, coefficient of variation, and intraclass correlation coefficient were 2.26 mL/kg/min, 4.90%, and 0.864, respectively. The positive and significant relationship between VO2max and CS suggests that the 3MT may be a practical alternative to predicting maximal oxygen uptake when time and access to a metabolic analyzer is limited.

1. Introduction

The use of physiological testing informs the sports performance coach and sports scientist about competitive athletic success as well as aids in development of endurance training programs by prescribing and monitoring training loads to elicit positive physiological adaptations. The applications of these measurements include the assessment of aerobic fitness, the prescription of exercise training workouts, and the prediction of endurance performance [1,2,3,4,5]. Maximal oxygen uptake (VO2max) [6] and other aerobic/anaerobic thresholds (i.e., lactate threshold (LT), maximal lactate steady state (MLSS), and their physiological equivalents) [4,7,8,9,10,11] are key indicators of endurance performance. However, traditional procedures and instrumentation to measure these variables are both time intensive and require expensive as well as sophisticated equipment. Laboratory measurements of VO2max and aerobic/anaerobic thresholds may take anywhere from 5 to 26 min [12,13,14] consisting of several stages and require access to costly equipment such as a metabolic analyzer. Not to mention, the accuracy of aerobic/anaerobic thresholds are influenced by several intrinsic and extrinsic variables [15,16,17] as well as various methodological techniques [18,19]. This has led to the use of several submaximal and field tests, as well as other technology, to assess endurance performance.
Both submaximal laboratory-based tests as well as field tests provide a means to overcome the barriers and prohibitive costs associated with measuring endurance performance. For instance, several submaximal tests only require access to a treadmill or cycle ergometer and may be used to accurately estimate VO2max in clinical [20], general [21], and athletic populations [22]. Further, field tests, performed outside of a laboratory setting, may be used to estimate both VO2max [23] and anaerobic thresholds, like the MLSS [24,25]. It has also been proposed that wearable technology may be used for estimating VO2max [26]. However, the accuracy of such devices still warrants further investigation [27]. Though these seem like feasible alternatives to the expensive laboratory measurements, the testing procedures are still time intensive and may require complex equations with multiple variables [28]. If time is a constraint, the Queen’s college step test is one of the most feasible and time-efficient tests, totaling only 3 min. However, it only provides an estimate of VO2max [29]. Therefore, a short field test that could non-invasively assess both fractional and maximal threshold values with comparable accuracy to that of metabolic testing would be a valuable aid in optimizing training of recreational and competitive endurance athletes.
The 3 minute all-out test (3MT) may be the most cost and time-efficient means of assessing both fractional and maximal threshold values. The 3MT is short in duration and has demonstrated to be both a reliable and valid test for the measurement of critical speed (CS) and the finite capacity of running speeds above CS, D prime (D’) [30,31]. Critical speed has demonstrated a relationship to both fractional and maximal threshold values [18,32,33,34,35]. A relationship exists between CS and MLSS [32], although CS may be a more sensitive and reliable fractional threshold measurement of the upper limit of the heavy exercise intensity domain [18,33]. Evidence also suggests that CS and VO2max are positively correlated, meaning that those with a higher VO2max also have a greater CS value [34,35]. This relationship is further displayed, as VO2max measured from incremental stage tests was similar to maximal values attained by traditional CS testing [36]. These findings are additionally substantiated by similar VO2max values obtained from a graded exercise test (GXT) to both a traditional running and shuttle 3MT [37]. However, few studies have examined the ability of CS to predict VO2max [34,38]. Moritani et al. [38] and Kendall et al. [34] have revealed that VO2max may be derived from regression equations using the cycling the rowing equivalents of CS and D’ from traditional CS testing. Traditional CS testing consists of several exercise trial measurements, sometimes across multiple days, at various intensities or distances, which may take longer than a GXT to measure VO2max [36,38,39,40]. Thus, the 3MT may provide a practical alternative to measuring both CS and VO2max in a fraction of the time.
To our knowledge, no other short duration field test (<3 min), requiring minimal equipment, provides both fractional and maximal threshold values as can be determined in a laboratory setting using a GXT or multi-stage test. Hence, the aim of this study was to determine the relationship between CS and D’ from a running 3MT and VO2max from a GXT to develop a regression equation to predict relative VO2max based on significant independent variables (CS and D’). The purpose was to determine whether the 3MT may be a practical testing alternative in identifying aerobic fitness within individuals when access to expensive instrumentation is restricted. It was hypothesized that there would be a significant relationship between CS and VO2max as well as D’ and VO2max when assessed from the 3MT and GXT, respectively.

2. Materials and Methods

2.1. Participants

Thirty-seven men and women were originally recruited from a local high-intensity functional training (HIFT) gym. Twelve participants were excluded from the final analysis due to invalid tests discussed below. Thus, final analysis was conducted on twenty-five recreational HIFT athletes. Participant demographic data was collected on the first visit and is displayed in Table 1. All participants provided written informed consent and the protocol under which this study was conducted was approved by the Point Loma Nazarene University Institutional Review Board (ID# 17204). Participants recruited were between the ages of 18 and 45, healthy and uninjured, and had at least 1 year of exercise training experience providing them the ability to perform standard HIFT workouts without scaling or modifications [41]. Normative data collected on two standardized workouts that participants completed in this study indicated both men and women to both be within approximately the 30th and 60th percentiles of competitive workout performance, representing a recreationally competitive group [42].

2.2. Protocol

All testing procedures took place at Point Loma Nazarene University within the Exercise Physiology Lab and the Track and Field Stadium. Upon completion of descriptive assessments, participants completed a 3MT and GXT with a supramaximal square-wave verification phase (VP) on separate days with at least 48 h between each test to allow sufficient recovery time for the participants [43]. Participants were required to be 2–3 h fasted having had no caffeine prior to testing [44].

2.3. Body Composition and Anthropometrics

Each participant underwent a series of measurements evaluating body composition and anthropometrics. Height was measured with participants standing barefoot on a stadiometer (Seca Inc. Hamburg, Germany). Air displacement plethysmograph via the BodPod (Cosmed, Concord, CA, USA) was used to assess body composition where body fat percentage was calculated using Siri’s formula [45]. The BodPod has demonstrated to be both a valid and reliable measurement of body composition and was calibrated daily before testing according to the manufacturer’s instructions [45,46].

2.4. Graded Exercise Test

All participants completed an initial ramp GXT followed by a supramaximal square-wave VP. Due to high volume of testing, two Parvo Medics TrueOne 2400 (Parvo Medics, Sandy, UT, USA) metabolic analyzers were used to continuously collect gas exchange values, while heart rate was measured with a Polar heart rate monitor (Polar, Lake Success, NY, USA). Flowmeter and gas calibrations were performed prior to each test as per the manufacturer’s instructions. Previous findings have revealed low interunit errors of 1.5–2.1% [47] and utilizing two units allowed for two participants to be tested at the same time. If repeat tests were needed the same unit was used within a subject. After sampling resting data for 2 min, participants began a 5 min walking warm up between 3.0 and 3.5 mph. Upon completion of the warm up, the individualized custom treadmill GXT commenced where participants started at 5 mph with a 3% grade and each minute the speed increased with the grade remaining constant. The custom GXT protocol was created using a non-exercise regression equation to predict VO2max and then deriving a speed estimate with that metabolic value [48]. The estimated peak was then divided by the number of stages to yield at GXT duration within 8–12 min [48]. Verbal encouragement was used as a form of extrinsic motivation to motivate participants to their maximum effort. The VO2max obtained from the initial ramp phase was derived as the average of the two-highest consecutive 15 s oxygen uptake averages. Upon completion of a 10 min walking active recovery between 1.5 and 3.5 mph, each subject performed a supramaximal square-wave verification test at 105% of the speed, with the same grade, obtained during the initial ramp test [49,50,51]. The initial ramp GXT VO2max and VP VO2max had to be within 3% for the GXT VO2max to be accepted as a true max [52]. If greater than 3%, participants came back to perform the test again on the same unit, or their results were excluded from the final analysis.
Results from the GXT were used to identify the speed evoking gas exchange threshold (GET) and VO2max using a linear interpolation method [53]. The GET was determined using the v-slope method [54]. The physiological response to a given change in speed during a GXT is not instantaneous, rather, it is delayed typically by 1 min. Hence, the speed evoking a specific gas exchange value is associated with the specific speed preceding the measurement by 1 min. To calculate this, speed (mph) equaled the incremental stage change value divided by four, as data was averaged every 15 s and stage speed increased each minute. These calculations were used to determine the average of the speeds at GET and VO2max (50% Δ) to confirm CS results from the 3MT [30]. Calculating 50% Δ allowed us to screen for pacing during the 3MT as CP is the approximate mean value (50% Δ) for power evoking gas exchange threshold (GET) and VO2max, as determined from a GXT [31].

2.5. 3 Minute All-Out Test

The 3MT was conducted on a flat track following a general running warm up and standardized dynamic stretching routine created by a Certified Strength and Conditioning Specialist. Participants ran as fast as possible around the track for 3 min and 5 s as a GPS tracking app, Sports Tracker (Amer Sports Digital Services Oy, Vantaa, Finland), collected second-by-second speed data which was used to derive CS and D’. The running 3MT presumes that an athlete will expend D’ within 2.5 min of all-out effort, whereby the mean speed between 2.5 and 3.0 min will reach a nadir at CS [30]. Thus, D’ from a running 3MT was derived by Equation (1), where time (t) equals 150 s, S150 s (m/s) equals the average speed for the first 150 s, and CS (m/s) is the average speed between 150 s and 180 s [30].
D′ = t (S150s − CS)
As previously mentioned, 50% Δ, calculated as the average of the speeds at GET and VO2max, was used to detect pacing during the 3MT. A CS considerably different from 50% Δ (≥3.5%) denoted an inaccurate and/or inflated CS due to pacing and warranted retesting or exclusion of that data. As the GXT protocol consisted of running at a constant 3% grade, a series of regression equations were utilized to convert treadmill speed and grade to equivalent outdoor running speed [55].

2.6. Statistical Analysis

Preliminary analyses were conducted to ensure no violation of the assumptions of performing a regression analysis. Cook’s distance and box plots were used to inspect outliers. The Kolmogorov–Smirnov Statistic and normal P–P plots were used to assess normality, linearity using scatterplots, and multicollinearity from values of tolerance and variance inflation factor. Lastly, homoscedasticity was evaluated using a scatterplot of standardized residuals and predicted values. Descriptive statistics were performed on participant characteristics. Data analyses were performed using SPSS Version 25. Pearson’s product moment correlation analyses were completed to determine the strength of the relationship between the CS, D’, and observed relative VO2max values. A multiple stepwise linear regression was used to determine the relative influence of significant correlative data and sex, creating a prediction equation to determine relative VO2max.
Relative consistency between tests was evaluated using intraclass correlation coefficients (ICC α) whereas absolute consistency was evaluated using coefficient of variation (CV%) and typical error (TE) [56]. Comparisons between observed and predicted VO2max as well as 50% Δ and CS were made using paired t tests. The level of significance (α-level) for statistical analysis was set at p < 0.05. All data is reported as the mean ± standard deviation (SD).

3. Results

Preliminary analyses revealed no violations, ensuring all assumptions were met for a regression analysis. However, as previously mentioned, twelve participants were excluded from the final analysis due to one of the following: inaccurate and/or inflated CS due to pacing on the 3MT (9 participants) or a VP greater than 3% (3 participants). Relative VO2max (47.71 ± 6.54 mL/kg/min) and VO2 verification (47.18 ± 6.19 mL/kg/min) displayed internal consistency (CV% = 0.96, TE = 0.45 mL/kg/min). An average difference of only 1.0% between the ramp and verification protocols indicated participants reached a true VO2max [49,52,57,58]. Lastly, no significant difference was detected between 50% Δ (3.59 ± 0.48 m/s) and CS (3.56 ± 0.55 m/s) (t = 0.524, p = 0.605).
The Pearson’s product-moment correlation coefficient displayed a strong significant positive correlation between CS and VO2max (r = 0.819, p < 0.001) but not between D’ and VO2max (D’ = 208.5 ± 54.3 m; r = −0.198, p = 0.344). A stepwise linear regression was used to generate a prediction equation (Equation (2)) for determining relative VO2max from CS and sex.
Relative VO2max = 8.449(CS) + 4.387(F = 0, M = 1) + 14.683, SEE = 3.34 mL/kg/min
In Model 1, CS explained 67%. Model 2 added sex, which explained 76% of the variance in the model, F (1, 22) = 8.273 p = 0.009. Results for relative and absolute consistency are displayed in Table 2. Lastly, results from the regression analysis are displayed in Figure 1 and Table 3.

4. Discussion

4.1. Summary

The objective of the present study was to determine whether the 3MT may be a practical testing alternative in identifying aerobic fitness within individuals and develop a regression equation to predict VO2max. Significant correlations displayed between CS and relative VO2max (r = 0.819, p < 0.001) are in line with other studies identifying statistically significant positive correlations between CS and VO2max [34,35,38]. The positive correlations represent that individuals with a greater CS value also had a higher VO2max. Sex differences are well established, demonstrating that women may have a lower VO2max compared to equally trained male counterparts due to body size and composition as well as cardiorespiratory differences [59]. Therefore, sex was included in the regression analysis. The primary finding of this study was the predictive strength of CS and sex for VO2max in HIFT athletes as both CS and sex displayed significant β weights, displayed in Table 2. No significant difference was displayed between the observed VO2max from the GXT and the predicted VO2max from the model. High internal consistency between the values was also observed as displayed in Table 3. Findings support the use of CS, calculated from a 3MT, and sex, to predict VO2max.

4.2. Using the 3MT to Predict VO2max and its Applications

To our knowledge, no previous studies have provided a regression equation for CS to predict VO2max, specifically using the 3MT. Moritani et al. [38] developed a regression equation to predict VO2max based on cycling CP parameters determined by using the cycle ergometer in college students. The regression analysis revealed that VO2max may be predicted by CP and anaerobic working capacity (AWC/W’) with a standard error of the estimate (SEE) of 0.241 L/min [38]. In rowers, Kendall et al. [34] also demonstrated CV and ARC may be used to predict VO2max with a SEE of 0.161 L/min. However, it is important to note that participants in each study completed multiple trial measurements for the attainment of CP/CV and W’/ARC and not the 3MT. Moritani et al. [38] had male participants cycle at 400, 350, 300, and 275 W, while female participants cycled at 300, 250, 200, and 175 W until the onset of fatigue. Kendall et al. (2012) conducted measurements on two separate days. On day one, rowers completed distances of 400 and 1,000 m and 48 h later the rowers completed 600 and 800 m distances with 15 min between each measurement [34]. This makes for a less time efficient measurement than measuring VO2max with a metabolic analyzer. The 3MT, which may also be used across multiple exercise modalities, provides measurements of CS and D’ like the multiple measurement method and has demonstrated to be a valid and reliable test for multiple modalities [30,60,61,62]. The 3MT also does not require a preliminary GXT before its implementation making it an effective alternative to assess larger numbers of athletes within a shorter span of time and decrease costs of performance testing [43].
It is important to note that in the current study, D’ did not demonstrate a significant relationship to VO2max, meaning that it did not contribute to the regression model as was displayed in previous studies [34,38]. It has been proposed that W’, and presumably D’, is an indicator of anaerobic capacity displaying a significant relationship to a variety of correlates like oxygen deficit, peak blood lactate concentration or mean power during a Wingate test have been investigated and found to be significant [63,64,65]. However, these findings have also been refuted [66,67]. This may be due to the fact that there are 2 and 3 parameter linear and non-linear models as well as an exponential model to estimate W’ [68,69]. Gaesser et al. [69] examined five models for calculating W’ revealing between-model correlations from 0.25 to 0.95. Bergstrom et al. [68] further examined these five mathematical models as well as the 3MT and demonstrated the lowest W’ to come from the 3MT (10 ± 2.6 kJ) and the highest W’ from the 3-parameter non-linear model (15.2 ± 5.6 kJ). Not only is W’ variable according to the method of testing but it may also be influenced by day-to-day variations in glycogen storage [70]. Given the inconsistencies in the calculations of W’, it may be presumed that D’ would not be a good variable to include in a predictive equation. This benefits sports performance coaches and practitioners as the average speed of the last 30 s of the 3MT is the only calculation needed, simplifying the mathematical model for predicting relative VO2max.
Findings suggest that CS and sex may be used to estimate VO2max, therefore, providing a means of assessing aerobic fitness from the 3MT. By calculating CS and estimating VO2max, the 3MT provides both fractional and maximal threshold values making it one of the most impactful, cost-effective, and time-efficient tests to evaluate endurance performance. As previously mentioned, numerous field-based tests exist providing an affordable means to measure fractional and maximal threshold values. For instance, the University of Montreal Track Test [71] and Cooper 12 min Run test [23] use speed and distance, respectively, to estimate VO2max. Several constant velocity tests, with the collection of blood lactate measurements, are used to determine fractional thresholds like LT and MLSS [24,72]. However, field-based testing methods are still time consuming and have been shown to lack accuracy, especially in athletic populations [26,73,74]. If time is a constraint, it may be argued that step tests may be used to estimate VO2max [75], consequently, this this would require another test to assess a fractional threshold value. Given that VO2max [6] and CS [10,40] are key indicators of endurance performance, the implementation of the 3MT to calculate both variables provides a practical means of measurement.

4.3. Practical Applications for Exercise Prescription

Beyond indicators of endurance performance, both CS and VO2max may be used for exercise prescription. Exercise intensity is commonly prescribed a percentage of VO2max (%VO2max). It has been proposed that %VO2max should be used to prescribe exercise within the moderate domain of exercise intensity (i.e., below LT) [76] as it is less likely to elicit individual variations in blood lactate accumulation [77,78,79] which may result in too low or too high of a metabolic intensity. On the contrary, it has been noted that CS may be a more significant indicator of endurance performance than VO2max [6]. The CS concept mathematically defines the relationship between speed and time to exhaustion allowing for the precise estimation of time trial completion within approximately 2.5 to 18 min [80,81]. Though these calculations are specific within the severe domain of exercise intensity, it has been proposed that taking 90% of CS may be used to predict longer time trial events [30]. Given this relationship between speed and time, the CS concept may also be used for the prescription of high-intensity interval training [82]. Further details explaining the practical application of the CS concept are presented by Pettitt [81]. Thus, the 3MT provides data that may be used to assess aerobic fitness, prescribe exercise within specific intensity domains, and predict endurance time trial performance.

4.4. High-Intensity Functional Training

Findings support the use of the 3MT as a less expensive and more time efficient means of determining CS and VO2max, specifically in HIFT athletes. This form of training and competition emphasizes training a variety of functional movements, at a high-intensity, using minimal equipment to enhance parameters of general physical fitness [83,84]. Due to this methodology, facilities where these athlete’s train do not have access to expensive equipment necessary to evaluate and enhance key physiological performance variables, specifically VO2max.
HIFT has rapidly grown in popularity and has expanded to where athletes participate in competitive fitness competitions both online as well as in person at local, regional, and international events [85,86]. In these competitions, athlete’s compete in workouts that may consist of multiple exercise modalities performing workouts as fast as possible for time, for as many repetitions as possible in an allotted time, or for maximal weight lifted. A large variety of physiological factors have previously been identified that influence HIFT athlete’s performance, with VO2max being a significant predictor of competitive success [41,87,88,89,90,91,92]. Bellar et al. [90] identified VO2max to be a significant indicator of workout performance by revealing a positive and significant correlation between maximum aerobic capacity and the amount of repetitions performed in a timed workout. This was further supported by findings in our laboratory [41] as well as Martinez-Gomez et al. [93] who also demonstrated VO2max to be a significant indicator of HIFT workout performance. However, it is important to note that VO2max as an indicator of HIFT workout performance may be mode specific. Maximal oxygen uptake was previously assessed on a longer HIFT workout. However, the workout did not consist of the mode that the GXT was conducted on, which was on a cycle ergometer; the workout consisted of push-ups, pull-ups, and air squats for as many repetitions as possible in 20 min [89]. Thus, it is important to emphasize specificity in testing and workout modalities. The significant relationship of VO2max to HIFT performance is an important reason to implement a 3MT for the prediction of VO2max. This does not mean that CS may not also play a role in HIFT workout performance. Future research should also investigate CS as a potential indicator of HIFT workout performance as critical power (CP) measured from a 3MT cycling test revealed that more competitive HIFT athletes have higher a higher CP [87]. Lastly, both VO2max and CS have previously demonstrated a significant relationship to HIFT workout performance on the same workout; however, VO2max significantly predicted performance [41].

4.5. Limitations and Future Research

This study is not without its limitations. Future research should investigate the use of this equation on a HIFT recreationally athletic population. Kendall et al. [34] assessed participants not used in the formation of the regression equation and determined the SEE to be 0.144 L/min. This study did not include a separate validation sample outside of the subjects used to develop the equation, therefore, future validation studies are warranted. Both males and females were used in the conception of the regression equation and sex significantly contributed to the model. The present study had a lower number of females and the sample size as a whole is small for building a regression equation. However, a post-hoc power analysis as well as low p-values and a high r-squared value indicate high statistical power and a strong prediction model.

5. Conclusions

In conclusion, the results of this study support the use of the 3MT as a practical field test for predicting VO2max in recreational HIFT athletes. The 3MT is a non-invasive field test and may contribute as a time effective alternative in which larger numbers of athletes can complete the procedure within a shorter span of time as compared to the time-intensive measuring of VO2max via a GXT. Future investigations should evaluate the validity of this equation as well as examine men and women separately due to physiological sex differences. The field-based method of the 3MT, along with the generated VO2max prediction equation, would allow coaches and training professionals to accurately predict VO2max in athletes while affording a practical method to predict, monitor, and refine performance in a timely manner.

Author Contributions

J.D.D. and S.J.B. were involved in the conceptualization and design of the methodology as well as formal analysis as well as preparation and writing of the manuscript. R.W.P. contributed to the methodology and formal analysis of the study as well as review and editing of the final manuscript. E.T.S. contributed to the methodology as well as the original draft preparation and editing of the manuscript. B.J.S. contributed to the conceptualization and methodology as well as preparation and editing of the manuscript. E.J. contributed to the methodology as well as the original draft preparation and editing of the manuscript. All authors have given approval to the final version of the manuscript.

Funding

This research received no external funding.

Acknowledgments

The authors would like to acknowledge the athletes that participated in this study as well as the owners and coaches at CrossFit® Fortius®.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Beltz, N.M.; Gibson, A.L.; Janot, J.M.; Kravitz, L.; Mermier, C.M.; Dalleck, L.C. Graded exercise testing protocols for the determination of VO2max: Historical perspectives, progress, and future considerations. J. Sports Med. 2016, 1–12. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Garber, C.; Blissmer, B.; Deschenes, M.; Franklin, B.; Lamonte, M.; Lee, I.; Nieman, D.; Swain, D. ACSM Position Stand. Cardiorespiratory, Musculoskeletal, and Neuromotor Fitness in Apparently Healthy Adults: Guidance for Prescribing Exercise. Med. Sci. Sports Exerc. 2011, 43, 1334–1359. [Google Scholar] [CrossRef] [PubMed]
  3. Cosgrove, M.; Wilson, J.; Watt, D.; Grant, S. The relationship between selected physiological variables of rowers and rowing performance as determined by a 2000 m ergometer test. J. Sports Sci. 1999, 17, 845–852. [Google Scholar] [CrossRef] [PubMed]
  4. McLaughlin, J.E.; Howley, E.T.; Bassett, D.R., Jr.; Thompson, D.L.; Fitzhugh, E.C. Test of the classic model for predicting endurance running performance. Med. Sci. Sports Exerc. 2010, 42, 991–997. [Google Scholar] [CrossRef]
  5. Costill, D.; Thomason, H.; Roberts, E. Fractional utilization of the aerobic capacity during distance running. Med. Sci. Sports 1973, 5, 248–252. [Google Scholar] [CrossRef]
  6. Joyner, M.J.; Coyle, E.F. Endurance exercise performance: The physiology of champions. J. Physiol. 2008, 586, 35–44. [Google Scholar] [CrossRef]
  7. Lourenço, T.F.; da Silva, F.O.; Tessutti, L.S.; da Silva, C.E.; Abad, C.C. Prediction of 3000-m Running Performance Using Classic Physiological Respiratory Responses. Int. J. Kinesiol. Sports Sci. 2018, 6, 18–24. [Google Scholar] [CrossRef]
  8. Bragada, J.A.; Santos, P.J.; Maia, J.A.; Colaço, P.J.; Lopes, V.P.; Barbosa, T.M. Longitudinal Study in 3000 m Male Runners: Relationship between Performance and Selected Physiological Parameters. J. Sports Sci. Med. 2010, 9, 439–444. [Google Scholar]
  9. Carlsson, M.; Carlsson, T.; Hammarström, D.; Malm, C.; Tonkonogi, M. Time trials predict the competitive performance capacity of junior cross-country skiers. Int. J. Sports Physiol. Perform. 2014, 9, 12–18. [Google Scholar] [CrossRef]
  10. Black, M.I.; Durant, J.; Jones, A.M.; Vanhatalo, A. Critical power derived from a 3-minute all-out test predicts 16.1-km road time-trial performance. Eur. J. Sport Sci. 2014, 14, 217–223. [Google Scholar] [CrossRef]
  11. Billat, L.V. Use of blood lactate measurements for prediction of exercise performance and for control of training. Sports Med. 1996, 22, 157–175. [Google Scholar] [CrossRef] [PubMed]
  12. Midgley, A.W.; Bentley, D.J.; Luttikholt, H.; McNaughton, L.R.; Millet, G.P. Challenging a dogma of exercise physiology. Sports Med. 2008, 38, 441–447. [Google Scholar] [CrossRef] [PubMed]
  13. Midgley, A.; McNaughton, L.; Carroll, S. Time at VO2max during Intermittent Treadmill Running: Test Protocol Dependent or Methodological Artefact? Int. J. Sports Med. 2007, 28, 934–939. [Google Scholar] [CrossRef]
  14. Kang, J.; Chaloupka, E.C.; Mastrangelo, M.A.; Biren, G.B.; Robertson, R.J. Physiological comparisons among three maximal treadmill exercise protocols in trained and untrained individuals. Eur. J. Appl. Physiol. 2001, 84, 291–295. [Google Scholar] [CrossRef] [PubMed]
  15. Jeukendrup, A.; Hesselink, M. Overtraining—What do lactate curves tell us? Brit. J. Sports Med. 1994, 28, 239–240. [Google Scholar] [CrossRef] [Green Version]
  16. Snyder, A.; Jeukendrup, A.; Hesselink, M.; Kuipers, H.; Foster, C. A physiological/psychological indicator of over-reaching during intensive training. Int. J. Sports Med. 1993, 14, 29–32. [Google Scholar] [CrossRef] [PubMed]
  17. Parkin, J.; Carey, M.; Zhao, S.; Febbraio, M. Effect of ambient temperature on human skeletal muscle metabolism during fatiguing submaximal exercise. J. Appl. Physiol. 1999, 86, 902–908. [Google Scholar] [CrossRef] [Green Version]
  18. Jones, A.M.; Burnley, M.; Black, M.I.; Poole, D.C.; Vanhatalo, A. The maximal metabolic steady state: Redefining the ‘gold standard’. Physiol. Rep. 2019, 7, e14098. [Google Scholar] [CrossRef] [Green Version]
  19. Mattioni Maturana, F.; Keir, D.A.; McLay, K.M.; Murias, J.M. Can measures of critical power precisely estimate the maximal metabolic steady-state? Appl. Physiol. Nutr. Metab. 2016, 41, 1197–1203. [Google Scholar] [CrossRef]
  20. Feasel, C.D.; Sandroff, B.M.; Motl, R.W. Cardiopulmonary Exercise Testing Using the Modified Balke Protocol in Fully Ambulatory People With Multiple Sclerosis. Cardiopulm Phys. Ther. J. 2020. [Google Scholar] [CrossRef]
  21. Björkman, F.; Ekblom-Bak, E.; Ekblom, Ö.; Ekblom, B. Validity of the revised Ekblom Bak cycle ergometer test in adults. Eur. J. Appl. Physiol. 2016, 116, 1627–1638. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Demirhan, B.; Cengiz, A.; Turkmen, M.; TEKBAŞ, B.; Cebi, M. Evaluating maximum oxygenuptake of male soccer players with bruce protocol. Sci. Move Health 2014, 14, 223–229. [Google Scholar]
  23. Penry, J.T.; Wilcox, A.R.; Yun, J. Validity and reliability analysis of Cooper’s 12-min run and the multistage shuttle run in healthy adults. J. Strength Cond. Res. 2011, 25, 597–605. [Google Scholar] [CrossRef] [PubMed]
  24. Llodio, I.; Gorostiaga, E.; Garcia-Tabar, I.; Granados, C.; Sánchez-Medina, L. Estimation of the maximal lactate steady state in endurance runners. Int. J. Sports Med. 2016, 37, 539–546. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Carminatti, L.J.; Batista, B.N.; da Silva, J.F.; Tramontin, A.F.; Costa, V.P.; de Lucas, R.D.; Borszcz, F.K. Predicting Maximal Lactate Steady State from Carminatti’s Shuttle Run Test in Soccer Players. Int. J. Sports Med. 2020. [Google Scholar] [CrossRef]
  26. Sartor, F.; Vernillo, G.; De Morree, H.M.; Bonomi, A.G.; La Torre, A.; Kubis, H.-P.; Veicsteinas, A. Estimation of maximal oxygen uptake via submaximal exercise testing in sports, clinical, and home settings. Sports Med. 2013, 43, 865–873. [Google Scholar] [CrossRef]
  27. Freeberg, K.A.; Baughman, B.R.; Vickey, T.; Sullivan, J.A.; Sawyer, B.J. Assessing the ability of the Fitbit Charge 2 to accurately predict VO2max. MHealth 2019, 5. [Google Scholar] [CrossRef]
  28. Buttar, K.K. A review: Maximal oxygen uptake (VO2max) and its estimation methods. IJPESH 2019, 6, 24–32. [Google Scholar]
  29. Nabi, T.; Rafiq, N.; Qayoom, O. Assessment of cardiovascular fitness [VO2max] among medical students by Queens College step test. Int. J. Biomed. Adv. Res. 2015, 6, 418–421. [Google Scholar]
  30. Pettitt, R.W.; Jamnick, N.; Clark, I.E. 3-min all-out exercise test for running. Int. J. Sports Med. 2012, 33, 426–431. [Google Scholar] [CrossRef]
  31. Burnley, M.; Doust, J.H.; Vanhatalo, A. A 3-minute all-out test to determine peak oxygen uptake and the maximal steady state. Med. Sci. Sports Exerc. 2006, 38, 1995–2003. [Google Scholar] [CrossRef] [PubMed]
  32. Smith, C.G.; Jones, A.M. The relationship between critical velocity, maximal lactate steady-state velocity and lactate turnpoint velocity in runners. Eur. J. Appl. Physiol. 2001, 85, 19–26. [Google Scholar] [CrossRef] [PubMed]
  33. Vanhatalo, A.; Doust, J.; Burnley, M. Determination of critical power using a 3-min all-out cycling test. Med. Sci. Sports Exerc. 2007, 39, 548–555. [Google Scholar] [CrossRef] [PubMed]
  34. Kendall, K.L.; Fukuda, D.H.; Smith, A.E.; Cramer, J.T.; Stout, J.R. Predicting maximal aerobic capacity (VO2max) from the critical velocity test in female collegiate rowers. J. Strength Cond. Res. 2012, 26, 733–738. [Google Scholar] [CrossRef]
  35. Santos, T.M.; Rodrigues, A.I.; Greco, C.C.; Marques, A.L.; Terra, B.S.; Oliveira, B.R.R. Estimated VO2max and its corresponding velocity predict performance of amateur runners. Braz. J. Kinanthropom Hum. Perform. 2012, 14, 192–201. [Google Scholar]
  36. Cheng, C.-F.; Yang, Y.-S.; Lin, H.-M.; Lee, C.-L.; Wang, C.-Y. Determination of critical power in trained rowers using a three-minute all-out rowing test. Eur. J. Appl. Physiol. 2012, 112, 1251–1260. [Google Scholar] [CrossRef]
  37. Kramer, M.; Du Randt, R.; Watson, M.; Pettitt, R.W. Oxygen uptake kinetics and speed-time correlates of modified 3-min all-out shuttle running in soccer players. PLoS ONE 2018, 13, e0201389. [Google Scholar] [CrossRef] [Green Version]
  38. Moritani, T.; Nagata, A.; Devries, H.A.; Muro, M. Critical power as a measure of physical work capacity and anaerobic threshold. Ergonomics 1981, 24, 339–350. [Google Scholar] [CrossRef]
  39. Kranenburg, K.J.; Smith, D.J. Comparison of critical speed determined from track running and treadmill tests in elite runners. Med. Sci. Sports Exerc. 1996, 28, 614–618. [Google Scholar]
  40. Nimmerichter, A.; Novak, N.; Triska, C.; Prinz, B.; Breese, B.C. Validity of treadmill-derived critical speed on predicting 5000-m track-running performance. J. Strength Cond. Res. 2017, 31, 706–714. [Google Scholar] [CrossRef]
  41. Dexheimer, J.D.; Schroeder, E.T.; Sawyer, B.J.; Pettitt, R.W.; Aguinaldo, A.L.; Torrence, W.A. Physiological Performance Measures as Indicators of CrossFit® Performance. Sports 2019, 7, 93. [Google Scholar] [CrossRef] [Green Version]
  42. Mangine, G.T.; Cebulla, B.; Feito, Y. Normative values for self-reported benchmark workout scores in crossfit® practitioners. Sports Med. Open 2018, 4, 39. [Google Scholar] [CrossRef]
  43. Dicks, N.D.; Jamnick, N.A.; Murray, S.R.; Pettitt, R.W. Load determination for the 3-min all-out exercise test for cycle ergometry. Int. J. Sports Physiol. Perform. 2016, 11, 197–203. [Google Scholar] [CrossRef]
  44. Kramer, M.; Watson, M.; Du Randt, R.; Pettitt, R.W. Critical speed as a measure of aerobic fitness for male rugby union players. Int. J. Sports Physiol. Perform. 2019, 14, 518–524. [Google Scholar] [CrossRef]
  45. Vescovi, J.D.; Zimmerman, S.L.; Miller, W.C.; Hildebrandt, L.; Hammer, R.L.; Fernhall, B. Evaluation of the BOD POD for estimating percentage body fat in a heterogeneous group of adult humans. Eur. J. Appl. Physiol. 2001, 85, 326–332. [Google Scholar] [CrossRef]
  46. Mathew, J.; Groth, B.; Horswill, C. Assessment of Reliability and Validity of Bod Pod in Body Composition Analysis. J. Kinesiol. Nutr. Stud. Res. 2015, 3. [Google Scholar]
  47. Macfarlane, D.; Wu, H. Inter-unit variability in two ParvoMedics TrueOne 2400 automated metabolic gas analysis systems. Eur. J. Appl. Physiol. 2013, 113, 753–762. [Google Scholar] [CrossRef] [Green Version]
  48. Pettitt, R.W.; Clark, I.E.; Ebner, S.M.; Sedgeman, D.T.; Murray, S.R. Gas exchange threshold and VO2max testing for athletes: An update. J. Strength Cond. Res. 2013, 27, 549–555. [Google Scholar] [CrossRef]
  49. Schaun, G.Z. The Maximal Oxygen Uptake Verification Phase: A Light at the End of the Tunnel? Sports Med. Open 2017, 3, 44. [Google Scholar] [CrossRef] [Green Version]
  50. Midgley, A.W.; McNaughton, L.R.; Carroll, S. Verification phase as a useful tool in the determination of the maximal oxygen uptake of distance runners. Appl. Physiol. Nutr. Metab. 2006, 31, 541–548. [Google Scholar] [CrossRef]
  51. Midgley, A.W.; Carroll, S.; Marchant, D.; McNaughton, L.R.; Siegler, J. Evaluation of true maximal oxygen uptake based on a novel set of standardized criteria. Appl. Physiol. Nutr. Metab. 2009, 34, 115–123. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Kirkeberg, J.; Dalleck, L.; Kamphoff, C.; Pettitt, R. Validity of 3 protocols for verifying VO2max. Int. J. Sports Med. 2011, 32, 266–270. [Google Scholar] [CrossRef] [PubMed]
  53. Kuipers, H.; Verstappen, F.; Keizer, H.; Geurten, P.; Van Kranenburg, G. Variability of aerobic performance in the laboratory and its physiologic correlates. Int. J. Sports Med. 1985, 6, 197–201. [Google Scholar] [CrossRef] [PubMed]
  54. Beaver, W.L.; Wasserman, K.; Whipp, B.J. A new method for detecting anaerobic threshold by gas exchange. J. Appl. Physiol. 1986, 60, 2020–2027. [Google Scholar] [CrossRef]
  55. Robergs, R.A. Simplified method and program for incremental exercise protocol development. J. Exerc. Physiol. Online 2007, 10, 1–23. [Google Scholar]
  56. Hopkins, W.G. Measures of reliability in sports medicine and science. Sports Med. 2000, 30, 1–15. [Google Scholar] [CrossRef] [Green Version]
  57. Poole, D.C.; Jones, A.M. Measurement of the maximum oxygen uptake VO2max: VO2peak is no longer acceptable. J. Appl. Physiol. 2017, 122, 997–1002. [Google Scholar] [CrossRef]
  58. Weatherwax, R.; Richardson, T.; Beltz, N.; Nolan, P.; Dalleck, L. Verification testing to confirm VO2max in altitude-residing, endurance-trained runners. Int. J. Sports Med. 2016, 37, 525–530. [Google Scholar] [CrossRef]
  59. Joyner, M.J. Physiological limits to endurance exercise performance: Influence of sex. J. Physiol. 2017, 595, 2949–2954. [Google Scholar] [CrossRef] [Green Version]
  60. de Aguiar, R.A.; Salvador, A.F.; Penteado, R.; Faraco, H.C.; Pettitt, R.W.; Caputo, F. Reliability and validity of the 3-min all-out running test. Rev. Bras. De Ciências Do Esporte 2018, 40, 288–294. [Google Scholar] [CrossRef]
  61. Piatrikova, E.; Sousa, A.C.; Gonzalez, J.T.; Williams, S. Validity and reliability of the 3-minute all-out test in national and International competitive swimmers. Int. J. Sports Physiol. Perform. 2018, 13, 1190–1198. [Google Scholar] [CrossRef]
  62. Clark, I.E.; Gartner, H.E.; Williams, J.L.; Pettitt, R.W. Validity of the 3-min all-out exercise test on the CompuTrainer. J. Strength Cond. Res. 2016, 30, 825–829. [Google Scholar] [CrossRef]
  63. Hill, D.W.; Smith, J.C. A comparison of methods of estimating anaerobic work capacity. Ergonomics 1993, 36, 1495–1500. [Google Scholar] [CrossRef]
  64. Miura, A.; Endo, M.; Sato, H.; Sato, H.; Barstow, T.J.; Fukuba, Y. Relationship between the curvature constant parameter of the power-duration curve and muscle cross-sectional area of the thigh for cycle ergometry in humans. Eur. J. Appl. Physiol. 2002, 87, 238–244. [Google Scholar] [CrossRef]
  65. Nebelsick-Gullett, L.J.; Housh, T.J.; Johnson, G.O.; Bauge, S.M. A comparison between methods of measuring anaerobic work capacity. Ergonomics 1988, 31, 1413–1419. [Google Scholar] [CrossRef]
  66. Buchheit, M.; Laursen, P.; Millet, G.; Pactat, F.; Ahmaidi, S. Predicting intermittent running performance: Critical velocity versus endurance index. Int. J. Sports Med. 2008, 29, 307–315. [Google Scholar] [CrossRef]
  67. Zagatto, A.M.; Papoti, M.; Gobatto, C.A. Anaerobic capacity may not be determined by critical power model in elite table tennis players. J. Sports Sci. Med. 2008, 7, 54. [Google Scholar]
  68. Bergstrom, H.C.; Housh, T.J.; Zuniga, J.M.; Traylor, D.A.; Lewis Jr, R.W.; Camic, C.L.; Schmidt, R.J.; Johnson, G.O. Differences among estimates of critical power and anaerobic work capacity derived from five mathematical models and the three-minute all-out test. J. Strength Cond. Res. 2014, 28, 592–600. [Google Scholar] [CrossRef]
  69. Gaesser, G.A.; Carnevale, T.J.; Garfinkel, A.; Walter, D.O.; Womack, C.J. Estimation of critical power with nonlinear and linear models. Med. Sci. Sports Exerc. 1995, 27, 1430–1438. [Google Scholar] [CrossRef]
  70. Miura, A.; Sato, H.; Sato, H.; hipp, B.J.; Fukuba, Y. The effect of glycogen depletion on the curvature constant parameter of the power-duration curve for cycle ergometry. Ergonomics 2000, 43, 133–141. [Google Scholar] [CrossRef]
  71. Pallarés, J.G.; Cerezuela-Espejo, V.; Morán-Navarro, R.; Martínez-Cava, A.; Conesa, E.; Courel-Ibáñez, J. A New Short Track Test to Estimate the VO2max and Maximal Aerobic Speed in Well-Trained Runners. J. Strength Cond. Res. 2019, 33, 1216–1221. [Google Scholar] [CrossRef]
  72. Castagna, C.; Manzi, V.; Impellizzeri, F.; Chaouachi, A.; Abdelkrim, N.B.; Ditroilo, M. Validity of an on-court lactate threshold test in young basketball players. J. Strength Cond. Res. 2010, 24, 2434–2439. [Google Scholar] [CrossRef]
  73. St Clair Gibson, A.; Broomhead, S.; Lambert, M.; Hawley, J. Prediction of maximal oxygen uptake from a 20-m shuttle run as measured directly in runners and squash players. J. Sports Sci. 1998, 16, 331–335. [Google Scholar] [CrossRef]
  74. Heydenreich, J.; Schutz, Y.; Kayser, B.; Melzer, K. Validity of the Actiheart step test for the estimation of maximum oxygen consumption in endurance athletes and healthy controls. Curr. Issues Sport. Sci. 2019, 4. [Google Scholar] [CrossRef] [Green Version]
  75. Bennett, H.; Parfitt, G.; Davison, K.; Eston, R. Validity of submaximal step tests to estimate maximal oxygen uptake in healthy adults. Sports Med. 2016, 46, 737–750. [Google Scholar] [CrossRef]
  76. Mann, T.; Lamberts, R.P.; Lambert, M.I. Methods of prescribing relative exercise intensity: Physiological and practical considerations. Sports Med. 2013, 43, 613–625. [Google Scholar] [CrossRef]
  77. Dwyer, J.; Bybee, R. Heart rate indices of the anaerobic threshold. Med. Sci. Sports Exerc. 1983, 15, 72–76. [Google Scholar] [CrossRef]
  78. Meyer, T.; Gabriel, H.; Kindermann, W. Is determination of exercise intensities as percentages of VO2max or HRmax adequate? Med. Sci. Sports Exerc. 1999, 31, 1342–1345. [Google Scholar] [CrossRef]
  79. Scharhag-Rosenberger, F.; Meyer, T.; Gäßler, N.; Faude, O.; Kindermann, W. Exercise at given percentages of VO2max: Heterogeneous metabolic responses between individuals. J. Sci. Med. Sport 2010, 13, 74–79. [Google Scholar] [CrossRef]
  80. Vanhatalo, A.; Jones, A.M.; Burnley, M. Application of critical power in sport. Int. J. Sports Physiol. Perform. 2011, 6, 128–136. [Google Scholar] [CrossRef] [Green Version]
  81. Pettitt, R.W. Applying the critical speed concept to racing strategy and interval training prescription. Int. J. Sports Physiol. Perform. 2016, 11, 842–847. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  82. Thomas, E.J.; Pettitt, R.W.; Kramer, M. High-Intensity Interval Training Prescribed Within the Secondary Severe-Intensity Domain Improves Critical Speed But Not Finite Distance Capacity. J. Sci. Sport Exerc. 2020, 2, 1–13. [Google Scholar] [CrossRef] [Green Version]
  83. Dawson, M.C. CrossFit: Fitness cult or reinventive institution? Int. Rev. Sociol. Sport 2017, 52, 361–379. [Google Scholar] [CrossRef]
  84. Feito, Y.; Heinrich, K.; Butcher, S.; Poston, W. High-Intensity Functional Training (HIFT): Definition and Research Implications for Improved Fitness. Sports 2018, 6, 76. [Google Scholar] [CrossRef] [Green Version]
  85. Glassman, G. Understanding Crossfit. Available online: http://library.crossfit.com/free/pdf/CFJ_56-07_Understanding.pdf (accessed on 22 October 2020).
  86. CrossFit®. Statistics From the 2016 Open. Available online: https://games.crossfit.com/video/statistics-2016-open (accessed on 23 January 2018).
  87. Mangine, G.T.; Stratton, M.T.; Almeda, C.G.; Roberts, M.D.; Esmat, T.A.; VanDusseldorp, T.A.; Feito, Y. Physiological differences between advanced CrossFit athletes, recreational CrossFit participants, and physically-active adults. PLoS ONE 2020, 15, e0223548. [Google Scholar] [CrossRef] [Green Version]
  88. Serafini, P.R.; Feito, Y.; Mangine, G.T. Self-reported measures of strength and sport-specific skills distinguish ranking in an international online fitness competition. J. Strength Cond. Res. 2018, 32, 3474–3484. [Google Scholar] [CrossRef]
  89. Butcher, S.J.; Neyedly, T.J.; Horvey, K.J.; Benko, C.R. Do physiological measures predict selected CrossFit(®) benchmark performance? Open Access J. Sports Med. 2015, 6, 241–247. [Google Scholar] [CrossRef] [Green Version]
  90. Bellar, D.; Hatchett, A.; Judge, L.W.; Breaux, M.E.; Marcus, L. The relationship of aerobic capacity, anaerobic peak power and experience to performance in CrossFit exercise. Biol. Sport 2015, 32, 315–320. [Google Scholar] [CrossRef]
  91. Martínez-Gómez, R.; Valenzuela, P.L.; Barranco-Gil, D.; Moral-González, S.; García-González, A.; Lucia, A. Full-Squat as a Determinant of Performance in CrossFit. Int. J. Sports Med. 2019, 40, 592–596. [Google Scholar] [CrossRef] [Green Version]
  92. Feito, Y.; Giardina, M.J.; Butcher, S.; Mangine, G.T. Repeated anaerobic tests predict performance among a group of advanced CrossFit-trained athletes. Appl. Physiol. Nutr. Metab. 2019, 44, 727–735. [Google Scholar] [CrossRef]
  93. Martínez-Gómez, R.; Valenzuela, P.L.; Alejo, L.B.; Gil-Cabrera, J.; Montalvo-Pérez, A.; Talavera, E.; Lucia, A.; Moral-González, S.; Barranco-Gil, D. Physiological Predictors of Competition Performance in CrossFit Athletes. Int. J. Environ. Res. Public Health 2020, 17, 3699. [Google Scholar] [CrossRef] [PubMed]
Sports 08 00155 g001 550
Figure 1. Plots of the relationship between CS and VO2max.
Figure 1. Plots of the relationship between CS and VO2max.
Sports 08 00155 g001
Table
Table 1. Participant Characteristics.
Table 1. Participant Characteristics.
TotalMalesFemales
N
Age (years)		25
27.6 ± 4.5		17
27.6 ± 5.1		8
27.5 ± 3.3
Height (cm)174.5 ± 18.3178.8 ± 20.3165.4 ± 8.6
Weight (kg)77.4 ± 14.883.5 ± 12.964.5 ± 9.1
Body Fat (%)15.7 ± 6.513.3 ± 6.421.1 ± 2.6
Note: The values are express as the mean ± standard deviation (SD).
Table
Table 2. VO2max prediction equation measures of reliability Model 2.
Table 2. VO2max prediction equation measures of reliability Model 2.
Observed VO2max (ml/kg/min−1) †Predicted VO2max (ml/kg/min−1) †TE
(mL/kg/min−1)		%CVICCLower 95% CI (ml/kg/min−1)Upper 95% CI (ml/kg/min−1)
47.71 ± 6.54 a47.71 ± 5.70 a2.264.900.8641.773.15
a Not significantly different (t = −0.000, p = 1.000). Abbreviations: TE = typical error; %CV = coefficient of variation; ICC = intraclass correlation coefficient; CI = confidence interval. † Values are given as the mean ± SD.
Table
Table 3. Summary of Regression Analysis Model 2.
Table 3. Summary of Regression Analysis Model 2.
VariableBSEBβObserved Power
CS (m/s)8.4491.3230.709 **0.99
Sex (M/F)4.3871.5250.320 **
Note: ** p < 0.01; B = unstandardized regression coefficient; SEB = standard error of the coefficient; β = standardized coefficient; observed power = post-hoc power analysis.
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Dexheimer, J.D.; Brinson, S.J.; Pettitt, R.W.; Schroeder, E.T.; Sawyer, B.J.; Jo, E. Predicting Maximal Oxygen Uptake Using the 3-Minute All-Out Test in High-Intensity Functional Training Athletes. Sports 2020, 8, 155. https://doi.org/10.3390/sports8120155

AMA Style

Dexheimer JD, Brinson SJ, Pettitt RW, Schroeder ET, Sawyer BJ, Jo E. Predicting Maximal Oxygen Uptake Using the 3-Minute All-Out Test in High-Intensity Functional Training Athletes. Sports. 2020; 8(12):155. https://doi.org/10.3390/sports8120155

Chicago/Turabian Style

Dexheimer, Joshua D., Shane J. Brinson, Robert W. Pettitt, E. Todd Schroeder, Brandon J. Sawyer, and Edward Jo. 2020. "Predicting Maximal Oxygen Uptake Using the 3-Minute All-Out Test in High-Intensity Functional Training Athletes" Sports 8, no. 12: 155. https://doi.org/10.3390/sports8120155

APA Style

Dexheimer, J. D., Brinson, S. J., Pettitt, R. W., Schroeder, E. T., Sawyer, B. J., & Jo, E. (2020). Predicting Maximal Oxygen Uptake Using the 3-Minute All-Out Test in High-Intensity Functional Training Athletes. Sports, 8(12), 155. https://doi.org/10.3390/sports8120155

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