Abstract
ackground/Objectives:This study examines the utility of the 3-min all- out test (3MT) in determining exercise intensity domains through critical speed (CS), offering a practical alternative to traditionalVO2max -based methods.Methods:Eighteen trained runners completed both the 3MT and a graded treadmill test. CS,VO2max , and HRmax were measured and compared to markers such as ventilation threshold (VT), gas exchange threshold (GET), lactate threshold (LT), onset of blood lactate accumulation (OBLA), CS, andVO2max , which are threshold markers for defining six exercise intensity domains.Results:Results demonstrate that CS reliably distinguishes among six exercise intensity domains with greater precision and less variability than heart rate (HR) orVO2- derived metrics. Notably, HR was unable to differentiate between high-intensity domains, highlighting its limitations for performance prescription. Compared toVO2max testing, the 3MT offers a simpler, non-invasive, and time-efficient approach to identifying training zones and monitoring performance. CS effectively delineated moderate-to-heavy, heavy-to- severe, and severe-to-extreme intensity domains, with strong correlations to performance thresholds. The findings emphasize the accessibility and reliability of the 3MT, particularly in overcoming the logistical challenges of traditional testing methods.Conclusions:This research underscores the potential of the running 3MT as a valuable tool for individualized training prescription and performance monitoring in both
and monitoring performance. CS effectively delineated moderate-to-heavy, heavy-to- severe, and severe-to-extreme intensity domains, with strong correlations to performance thresholds. The findings emphasize the accessibility and reliability of the 3MT, particularly in overcoming the logistical challenges of traditional testing methods.Conclusions:This research underscores the potential of the running 3MT as a valuable tool for individualized training prescription and performance monitoring in both research and applied sports contexts, paving the way for a broader adoption in athletic training methodologies. Keywords:training zones; workout prescription; endurance; critical speed 1. Introduction Although the concept of the critical power model (CPM) in the realm of single-muscle testing was first established nearly 70 years ago by Monod and Scherrer [1], and an ex- tension to the model on athletes in the laboratory setting was initially done over 30 years ago by Moritani et al. [2], the concept of CPM has re-emerged and once again sparked the interest of the exercise physiology and human performance communities over the past decade. This renewed focus can be attributed to advancements in technology, such as more affordable power/speed-measuring devices, which have increased accessibility to the general public. The CPM is a non-invasive way to characterize an individual’s anaerobic and aerobic energy capacities. The aerobic component is labeled “Critical Power” (CP) and theoretically represents the power output that one could maintain indefinitely without reaching exhaustion, while the anaerobic work capacity (W ′ ) represents the finite supply of Physiologia2025,5, 6 https://doi.org/10.3390/physiologia5010006
Physiologia2025,5, 6 2 of 15 energy that is available for sustaining activity at intensities greater than critical power. Total W ′ depletion results in exhaustion, while partial depletion restricts the athlete’s maximal power output [3]. Despite the prominence of CPM, alternative models such as the anaerobic threshold (AT) and velocity atVO2max (vVO2max ) have also been widely used in exercise physiology. The AT model, which focuses on the point at which lactate begins to accumulate in the blood, has been a staple in the field for decades due to its ease of measurement and direct relationship to metabolic changes during exercise [4]. Similarly, thevVO2max model, which identifies the minimum speed at whichVO2max is achieved, provides valuable insights into an athlete’s endurance capacity [5]. However, both models face limitations: AT can be influenced by variations in lactate kinetics, andvVO2max is highly dependent on testing protocols, often leading to inconsistencies in application [5]. These limitations underscore the need for a model like CPM, which integrates both aerobic and anaerobic components into a unified framework. The technological improvements enabling more widespread data collection have facilitated the adoption of modeling techniques. In particular, tools such as the validated 3-min all-out test (3MT) can determine CP performed on a cycle ergometer [6,7], or on an athlete’s personal cycling equipment [8]. Irrespective of which equipment modality is used, the 3MT facilitates demarcation between the heavy and the severe exercise intensity domains [9,10]. The defining feature of the 3MT that makes it such a desirable tool in the exercise scientist’s proverbial “tool kit” lies in its ability to estimate CP and W ′ accurately in just one, time-efficient, single bout of exercise [7]. Moreover, the 3MT’s practicality is enhanced by validation studies demonstrating its reliability. Estimates of CP and W ′ derived from the 3MT are comparable to those obtained from traditional multi-day testing protocols, which often require several exhaustive ses- sions [11]. This single-session approach addresses the logistical and physical demands of traditional methods, making the 3MT particularly appealing for frequent performance assessments in both research and applied settings. By integrating CP and W ′ measurements
CP and W ′ derived from the 3MT are comparable to those obtained from traditional multi-day testing protocols, which often require several exhaustive ses- sions [11]. This single-session approach addresses the logistical and physical demands of traditional methods, making the 3MT particularly appealing for frequent performance assessments in both research and applied settings. By integrating CP and W ′ measurements into a concise format, the 3MT provides an accessible, efficient, and reliable method for evaluating critical intensity domains. Recent efforts have expanded CPM’s application beyond cycling, where the 3MT has also proven effective in estimating power at the moderate-to-heavy exercise inten- sity domains [6,7], more commonly defined by lactate and ventilatory thresholds [12]. Studies such as those by Iannetta et al. have further demonstrated that the 3MT can effectively predict metabolic responses and time-to-exhaustion across exercise intensity domains [13,14]. These capabilities highlight its utility in defining individualized training zones and optimizing race strategies across competitive levels. Therefore, the 3MT has emerged as a valuable approach to defining intensity domains in the sport of cycling [7,8], and may play an integral role in prescribing individualized training zones and in defining race strategy, in order to optimize athletic performance at all levels of sport. The application of the 3MT has expanded beyond cycling, and an adapted, analogous model of critical power in distance running suggests that these concepts may transcend into sports where power is too unwieldy to measure, but speed can be captured instead [15]. The work performed by Pettitt et al. (2012) demonstrated that a 3MT for running using critical speed (CS) as the analogous substitute for critical power and using D ′ —the fixed anaerobic distance that can be covered at speeds or intensities above CS—as the analogous substitute for anaerobic work capacity (W ′ ) in the CPM is feasible and holds great potential in providing the necessary data to predict running performances between the durations of approximately 2.5 and 18 min [7,15]. Similarly, studies by Busso et al. validate the reliability of CS and D ′ estimates for assessing exercise tolerance in the severe intensity domain [14].
for anaerobic work capacity (W ′ ) in the CPM is feasible and holds great potential in providing the necessary data to predict running performances between the durations of approximately 2.5 and 18 min [7,15]. Similarly, studies by Busso et al. validate the reliability of CS and D ′ estimates for assessing exercise tolerance in the severe intensity domain [14].
Physiologia2025,5, 6 3 of 15 Traditional laboratory-based methods for assessing intensity domains, such asVO2max , gas exchange, blood lactate testing, or heart rate (HR) monitoring, can be costly, inva- sive, and less precise at higher intensities [16]. Utilizing the average speed of the final 30 s (CS3MT ) captured from a running 3MT may aid coaches and exercise physiologists to define and prescribe individualized and uniquely specific training zones based on a given athlete’s CS. The benefits of using a 3MT are predicated based on its accuracy and reliability in determining the demarcation between heavy and severe exercise do- mains, its predetermined and finite testing duration, and its accessibility to the general population [7,17,18]. However, as highlighted by Iannetta et al., caution is needed in re- lying solely on HR for intensity demarcation due to its limitations in accurately defining transitions to severe and very severe intensity domains [13]. The plateauing of HR at high intensities underscores the advantages of performance-based measures like the 3MT in prescribing training intensities. Estimates of the same information from more traditionalVO2max testing are depen- dent on the protocol of the test (intensity–time slope, continuous vs. stages, work du- ration in stages protocol, etc.) [16,19,20]. For example,VO2max did not differ between continuous and staged protocols with durations ranging from 5 to 26 min; however, the intensity (speed/power output) associated withVO2max and test duration was significantly different [21,22]. Therefore, one must appreciate that the prescription of speed and corollary training intensities determined fromVO2max testing may differ with various test protocols. Finally, the running 3MT transcends the confines of utilizing HR data in the prescrip- tion of training zones. Although the HR response is linear during submaximal exercise loads, it plateaus (reaches its maximum) once the severe exercise domain is reached, and therefore, HR is ineffective in defining or prescribing the transition from the severe-to- very severe/extreme training intensity domains [23]. From this perspective, the running 3MT approach appears to be superior in providing reliable training intensity domains to prescribe athletes with workout regimens. In this study, the objective was to establish the exercise intensity domains in
exercise domain is reached, and therefore, HR is ineffective in defining or prescribing the transition from the severe-to- very severe/extreme training intensity domains [23]. From this perspective, the running 3MT approach appears to be superior in providing reliable training intensity domains to prescribe athletes with workout regimens. In this study, the objective was to establish the exercise intensity domains in athletes usingCS3MT derived from an indoor running 3MT, in an attempt to control for environ- mental influences. Building on previous findings that validate the efficacy of cycling 3MT to define training intensity domains, we hypothesize that a running 3MT performed on a 200 m indoor track will effectively define the transitions between the moderate-to-heavy, heavy-to-severe, and severe-to-very severe (i.e.,VO2max) exercise intensity domains [8]. 2. Results The results distinguish running training zone intensities developed with HR,VO2max , and CS, and compare the outcomes of each parameter. This paper demonstrates the potential of CS, calculated with a 3MT, to be a more appropriate method of training intensity determination and workout prescription thanVO2max and HR for male than for female. The boxplots using alternative variables [(VO2(L·min −1 ), speed (m·s −1 ) and heart rate (bpm)] (FigureA–F) illustrate both the ability to discriminate between training thresholds and the agreement of threshold levels. These plots show the medianVO2, speed, and heart rate for the 18 study participants (solid black line), along with the standard deviation (hatched line with barrel ends) for each lactate and ventilatory threshold of interest. Notably, the variance inVO2and heart rate at the lactate and ventilatory thresholds is greater than that observed for speed. Furthermore, the median speed not only exhibits less variance compared toVO2and heart rate but also provides a greater number of significantly distinct threshold values.
Physiologia2025,5, 6 4 of 15 Figure 1.Boxplot of the lactate and ventilation threshold for maleVO2(A), femaleVO2(B), male speed (C), female speed (D), male heart rate (E), and female heart rate (F). For each category of measures (VO2, speed, and HR), no significant differences were found between GET and LT values, except in heart rate for males andVO2for females. However, LT was significantly different from all other lactate and ventilatory thresholds of interest for speed and HR in females, as well as forVO2max and speed in females. Regarding heart rate and speed, the threshold values atVO2max , OBLA, and CS were indistinguishable from one another, resulting in the collapse of the exercise intensity domains into three groups for the females: mild, heavy, and severe. A similar grouping of three domains was observed for males, with thresholds demarcated at CS and OBLA for the heart rate. Threshold values assessed withVO2for females formed two broad categories, with OBLA serving as demarcation. For males, however, four groups emerged based onVO2max , CS, and GET/LT thresholds. Finally, speed in males was distinctly categorized into five groups: mild/moderate, heavy, very heavy, severe, and extreme. Among these, speed demonstrated tighter confidence intervals (5–7%) thanVO2(11–13%). Figure thresholds of interest, shown separately for males and females. Notably, the CS threshold occurs at 92% ofVO2max in males and is indistinguishable from OBLA at 89% for females. The shaded areas in Figure sizing its limitations in distinguishing certain intensity transitions. Specifically in males, HR cannot differentiate between the severe and extreme domains, nor between the heavy
Physiologia2025,5, 6 5 of 15 and very heavy domains. Similarly, in females, HR fails to distinguish the very heavy, severe, and extreme domains, as well as the mid and moderate domains. Figure 2.Exercise intensity domains based onHRmax,VO2max , and CS for male (left) and fe- male (right). ForVO2max , females exhibit only two broad exercise intensity domains: mild/moderate/heavy and very heavy/severe/extreme. In contrast, males have three domains (mild/moderate, heavy/very heavy, and severe/extreme). Conversely, Figure highlights the ability of bothVO2max and CS measures to define and to demarcate the distinct transitions between exercise intensity domains, spanning the entire spectrum of mild to extreme exercise intensity. 3. Materials and Methods 3.1. Experimental Approach to the Problem Speed measured at common exercise intensity indices (such as lactate threshold (LT), onset of blood lactate accumulation (OBLA), ventilation threshold (VT), gas exchange threshold (GET), and maximum oxygen uptake (VO2max )) that are established using lactate or metabolic analyzers are being used to demarcate between moderate and heavy exercise intensity domains [10], whereas the 3MT has been shown to provide an estimate of speed that separates the same domains [6,7]. The 3MT can be used as a single-session test to establish the transitions between both the moderate-to-heavy and the heavy-to-severe exercise intensity domains by determining the ratio of HR,VO2, and speed between these threshold indices (i.e., CS, LT, OBLA, VT, andVO2max ), and therefore, we chose to use this experimental approach to our study question. 3.2. Subjects A total of 18 healthy competitive runners (12 men and 6 women) volunteered in this study (mean±SD: age = 45.0±9.6 years; height = 175.9±9.9 cm;body mass = 67.7±11.6 kg ; VO2max = 52.3±7.2 mL ofO2·min −1 · kg −1 ). All the subjects were accustomed to high-intensity exercise and had been involved in running for 5–20 years. Prior to each session, subjects were requested not to undertake a strenuous workout in the preceding 48 h and refrain from caffeine and alcohol 3 h before reporting to the facility. They were informed about the aims, the procedures, and risks associated with the tests, and written informed consent was obtained
had been involved in running for 5–20 years. Prior to each session, subjects were requested not to undertake a strenuous workout in the preceding 48 h and refrain from caffeine and alcohol 3 h before reporting to the facility. They were informed about the aims, the procedures, and risks associated with the tests, and written informed consent was obtained
Physiologia2025,5, 6 6 of 15 from all subjects. This study was approved by the University of Toronto Review Ethics Board and was conducted in accordance with the Declaration of Helsinki. 3.3. Procedures Participants were screened using the PAR-Q and athlete consent form in person. PAR-Q+ is a physical activity readiness questionnaire that is used to determine safety and possible risks for an individual to begin an exercise program [24]. The questionnaire has been used in several studies that involve maximum efforts [25–27]. Once the written consent was obtained, subjects were familiarized with all the protocols and procedures. Subjects visited the indoor 200 m track and the laboratory on two occasions, respectively, with a minimum of 48 h of recovery between each test. For each subject, only one exercise test was conducted on a given day and all tests were completed within 14 d. The subjects exercised on an indoor 200 m track for the 3-min all-out test (3MT) to estimate the subjects’ CS and D ′ , and on a treadmill capable of adjusting 0.1% grade and 0.045 m·s −1 increments for the graded staged exercise test to measureVO2maxand lactate threshold. 3.4. 3-min All-Out Running Test The 3MT was conducted on a level indoor 200 m track. Before each trial, subjects performed their standard warm-up protocol lasting 10–20 min and then 5–10 min of rest. The subjects were instructed to rapidly build up to the maximal speed and maintain a maximum effort at all times throughout the entire test. Strong verbal encouragement was provided to motivate the participants to maintain maximal effort throughout the tests, however no elapsed time or speed feedback were given to the subjects during the test, in order to preclude subjects from pacing. To clarify, the experimenter was not blinded to the tests, as the 3MT was conducted in a team workout session for practical reasons. This setting enabled efficient data collection while adhering to the procedural standards for the study. Each athlete wore a wrist watch (Garmin Forerunner Model 620, Kansas City, KS, USA) and a heart rate strap in order to collect speed, heart
experimenter was not blinded to the tests, as the 3MT was conducted in a team workout session for practical reasons. This setting enabled efficient data collection while adhering to the procedural standards for the study. Each athlete wore a wrist watch (Garmin Forerunner Model 620, Kansas City, KS, USA) and a heart rate strap in order to collect speed, heart rate, and cadence data. Time splits were measured every 25 m to the nearest tenth of a second and the test was stopped when the last time split recorded was more than 3 min and 5 s to ensure gathering a full 3min of data. The end speed (ES) was determined as the mean speed during the final 30 s of the test, and the D ′ is estimated as the speed-time integral above the end speed [15]. 3.5. Graded Staged Exercise Test In matching the running speeds between track running and treadmill running, we examined the literature, which documents a higher energy cost of actual running compared to treadmill running due to air resistance [22]. Grade increases on the treadmill are reported to alter the relation, with one study reporting +1%·min −1 rise in treadmill grade at speeds above 3.98 m·s −1 being equivalent to a speed increase of 0.18 m·s −1 per 1 min stage, and a subsequent rate of 0.045 m·s −1 was subtracted from treadmill speed to approximate actual running [15]. This simple constant factor between treadmill and track was in contrast to the known effect of aerodynamic drag force which increases with the square of the speed. This effect will produce an increase in speed difference between treadmill and actual running for the same energy requirement (see Appendix). Similarly, Jones’ and Doust’s data showed an increasing slope betweenVO2and % grade with increasing speed, indicating that a constant translation of speed for 1% rise in grade is incorrect. A comprehensive method to determine the equivalent actual speed from treadmill speed could be achieved by utilizing the linear relationship betweenVO2and various treadmill grades and for road running [28]. However, the study was limited to a 3% grade
Description
The research highlights the 3MT's effectiveness in defining running intensity domains.