The Relationship Between the Moderate-Heavy Boundary and Critical Speed in Running

PURPOSE

Training characteristics such as duration, frequency, and intensity can be manipulated to optimize endurance performance, with an enduring interest in the role of training-intensity distribution to enhance training adaptations. Training intensity is typically separated into 3 zones, which align with the moderate-, heavy-, and severe-intensity domains. While estimates of the heavy- and severe-intensity boundary, that is, the critical speed (CS), can be derived from habitual training, determining the moderate-heavy boundary or first threshold (T1) requires testing, which can be costly and time-consuming. Therefore, the aim of this review was to examine the percentage at which T1 occurs relative to CS.

RESULTS

A systematic literature search yielded 26 studies with 527 participants, grouped by mean CS into low (11.5 km·h-1; 95% CI, 11.2-11.8), medium (13.4 km·h-1; 95% CI, 11.2-11.8), and high (16.0 km·h-1; 95% CI, 15.7-16.3) groups. Across all studies, T1 occurred at 82.3% of CS (95% CI, 81.1-83.6). In the medium- and high-CS groups, T1 occurred at a higher fraction of CS (83.2% CS, 95% CI, 81.3-85.1, and 84.2% CS, 95% CI, 82.3-86.1, respectively) relative to the low-CS group (80.6% CS, 95% CI, 78.0-83.2).

CONCLUSIONS

The study highlights some uncertainty in the fraction of T1 relative to CS, influenced by inconsistent approaches in determining both boundaries. However, our findings serve as a foundation for remote analysis and prescription of exercise intensity, although testing is recommended for more precise applications.

Modelling human endurance: power laws vs critical power

The power-duration relationship describes the time to exhaustion for exercise at different intensities. It is believed to be a "fundamental bioenergetic property of living systems" that this relationship is hyperbolic. Indeed, the hyperbolic (a.k.a. critical-power) model which formalises this belief is the dominant tool for describing and predicting high-intensity exercise performance, e.g. in cycling, running, rowing or swimming. However, the hyperbolic model is now the focus of a heated debate in the literature because it unrealistically represents efforts that are short (< 2 min) or long (> 15 min). We contribute to this debate by demonstrating that the power-duration relationship is more adequately represented by an alternative, power-law model. In particular, we show that the often-observed good fit of the hyperbolic model between 2 and 15 min should not be taken as proof that the power-duration relationship is hyperbolic. Rather, in this range, a hyperbolic function just happens to approximate a power law fairly well. We also prove mathematical results which suggest that the power-law model is a safer tool for pace selection than the hyperbolic model and that the former more naturally models fatigue than the latter.

Classification of Intensity in Team Sport Activity

PURPOSE

This study aimed to assess the efficacy of critical metabolic power derived from variable-speed movement for classifying intensity in team sport activity.

METHODS

Elite male hockey players (n = 12) completed a series of time trials (100 yards, 400 yards, 1500 yards) and a 3-min all-out test to derive both critical speed (CS) and critical power (CP). Heart rate (HR), blood lactate, and rating of perceived exertion were measured during each protocol. Participants (n = 10) then played two competitive hockey matches. Time spent greater than 85% of maximum HR was compared with time spent above CS (from the time trials) and CP (from the 3-min test).

RESULTS

Between protocols, there was a moderate and nonsignificant association for CS (r = 0.359, P = 0.252) and a very large association for CP (r = 0.754, P = 0.005); the association was very large for peak HR (r = 0.866, P < 0.001), large for blood lactate (r = 0.506, P = 0.093), and moderate for rating of perceived exertion (rho = 0.441, P = 0.152). Time trials produced higher CS (4.3 vs 2.0 m·s, P < 0.001) and CP (18.3 vs 10.5 W·kg, P < 0.001) values than did the 3-min test. In matches, there was a very large association between time spent above 85% of maximum HR and time spent above both CS (r = 0.719, P < 0.001) and CP (r = 0.867, P < 0.001). This relationship was stronger for CP compared with CS (Z = 3.29, P = 0.0007).

CONCLUSIONS

Speed is not an appropriate parameter for the classification of team sport activity comprising continual changes in speed and direction; however, critical metabolic power derived from variable-speed activity seems useful for this purpose.

Physical Qualities Pertaining to Shorter and Longer Change-of-Direction Speed Test Performance in Men and Women

This study investigated relationships between shorter (505, change-of-direction (COD) deficit as a derived physical quality) and longer (Illinois agility test; IAT) COD tests with linear speed, lower-body power (multidirectional jumping), and strength in recreationally-trained individuals. Twenty-one males and 22 females (similar to collegiate club-sport and tactical athletes) were assessed in: 505 and COD deficit from each leg; IAT; 20 m sprint; vertical jump (VJ height, peak anaerobic power measured in watts (PAPw), power-to-body mass ratio); standing broad jump; lateral jump (LJ) from each leg; and absolute and relative isometric midthigh pull (IMTP) strength. Partial correlations calculated sex-determined relationships between the COD and performance tests, with regression equations calculated (p < 0.05). The 505 and IAT correlated with all tests except PAPw and absolute IMTP (r = ±0.43–0.71). COD deficit correlated with the LJ (r = −0.34–0.60). Left- and right-leg 505 was predicted by sex, 20 m sprint, and left-leg LJ (70–77% explained variance). Right-leg COD deficit was predicted by sex and left-leg LJ (27% explained variance). IAT was predicted by sex, 20 m sprint, right-leg LJ, and relative IMTP (84% explained variance). For individuals with limited training time, improving linear speed, and relative lower-body power and strength, could enhance shorter and longer COD performance.

Strength training increases endurance time to exhaustion during high-intensity exercise despite no change in critical power

The purpose of this study was to determine whether improvements in endurance exercise performance elicited by strength training were accurately reflected by changes in parameters of the power-duration hyperbola for high-intensity exercise. Before and after 8 weeks of strength training (N = 14) or no exercise, control (N = 5), 19 males (age: 20.6 ± 2.0 years; weight: 78.2 ± 15.9 kg) performed a maximal incremental exercise test on a cycle ergometer and also cycled to exhaustion during 4 constant-power exercise bouts. Critical power (CP) and anaerobic work capacity (W') were estimated using nonlinear and linear models. Subjects in the strength training group improved significantly more than controls (p < 0.05) for strength (~30%), power at V[Combining Dot Above]O2peak (7.9%), and time to exhaustion (TTE) for all 4 constant-power tests (~39%). Contrary to our hypothesis, CP did not change significantly after strength training (p > 0.05 for all models). Strength training improved W' (mean range of improvement = +5.8 to +10.0 kJ; p < 0.05) for both linear models. Increases in W' were consistently positively correlated with improvements in TTE, whereas changes in CP were not. Our findings indicate that strength training alters the power-duration hyperbola such that W' is enhanced without any improvement in CP. Consequently, CP may not be robust enough to track changes in endurance capacity elicited by strength training, and we do not recommend it to be used for this purpose. Conversely, W' may be the better indicator of improvement in endurance performance elicited by strength training.

Fast-start strategy increases the time spent above 95 %VO2max during severe-intensity intermittent running exercise

This study aimed to use the intermittent critical velocity (ICV) model to individualize intermittent exercise and analyze whether a fast-start strategy could increase the time spent at or above 95 %VO(2max) (t95VO(2max)) during intermittent exercise. After an incremental test, seven active male subjects performed three intermittent exercise tests until exhaustion at 100, 110, and 120 % of the maximal aerobic velocity to determine ICV. On three occasions, the subjects performed an intermittent exercise test until exhaustion at 105 % (IE105) and 125 % (IE125) of ICV, and at a speed that was initially set at 125 %ICV but which then decreased to 105 %ICV (IE125-105). The intermittent exercise consisted of repeated 30-s runs alternated with 15-s passive rest intervals. There was no difference between the predicted and actual Tlim for IE125 (300 ± 72 s and 284 ± 76 s) and IE105 (1,438 ± 423 s and 1,439 ± 518 s), but for IE125-105 the predicted Tlim underestimated the actual Tlim (888 ± 211 s and 1,051 ± 153 s, respectively). The t95VO(2max) during IE125-105 (289 ± 150 s) was significantly higher than IE125 (113 ± 40 s) and IE105 (106 ± 71 s), but no significant differences were found between IE125 and IE105. It can be concluded that predicting Tlim from the ICV model was affected by the fast-start protocol during intermittent exercise. Furthermore, fast-start protocol was able to increase the time spent at or above 95 %VO2max during intermittent exercise above ICV despite a longer total exercise time at IE105.