Anaerobic Capacity in Running: The Effect of Computational Method

The effect of exercise hyperpnea on gross efficiency and anaerobic capacity estimates during a 3-min cycle time trial

This study aimed to analyze the effect of exercise-induced hyperpnea on gross efficiency (GE) and anaerobic capacity estimates during a self-paced 3-min supramaximal cycle time trial (TT). Fourteen highly trained male cyclists performed 7 × 4-min submaximal stages, a 6-min passive rest, a 3-min TT, a 5-min passive rest, and a 6-min submaximal stage. Three models were based on the 7 × 4-min linear regression extrapolation method, using 1) the conventional model (7-Y_LIN); 2) the same 7-Y_LIN model but correcting for the additional ventilatory cost (i.e., hyperpnea) (7-Y_LIN-V-cor); and 3) accounting for linearly declining GE during the TT (7-Y_LIN-D). The other three models were based on GE from the last submaximal stage, using the conventional model (GE_LAST) and the same modifications as described for 7+Y_LIN, i.e., 1) GE_LAST, 2) GE_LAST-V-cor, and 3) GE_LAST-D. The GE_LAST model generated 18% higher values of anaerobic capacity than the 7-Y_LIN model (P < 0.05). During the TT, the hyperpnea-corrected model (i.e., 7-Y_LIN-V-cor or GE_LAST-V-cor) generated, compared with the respective conventional model (i.e., 7-Y_LIN or GE_LAST), ∼0.7 percentage points lower GE and ∼11% higher anaerobic capacity (all, P < 0.05). The post-TT GE was 1.9 percentage points lower (P < 0.001) and the 7-Y_LIN-D or GE_LAST-D model generated, compared with the respective conventional model, a lower GE (∼1.0 percentage points) and ∼17% higher anaerobic capacity during the TT (all, P < 0.05). In conclusion, the correction for a declining GE due to hyperpnea during a supramaximal TT resulted in an increased required total metabolic rate and anaerobic energy expenditure compared with the conventional models.NEW & NOTEWORTHY This study demonstrates that GE declines during a 3-min supramaximal cycle TT, which is possibly related to the hyperpneic response during supramaximal exercise. The finding from this study also provides novel insight into how the increased ventilatory energy cost from exercise-induced hyperpnea contributes to decreased GE, increased required total metabolic rate, and increased anaerobic energy expenditure during supramaximal exercise. Therefore, conventional linear models for estimating anaerobic capacity are likely to generate underestimated values.

Anaerobic work capacity in cycling: the effect of computational method

Purpose

To compare the anaerobic work capacity (AnWC, i.e., attributable anaerobic mechanical work) assessed using four different approaches/models applied to time-trial (TT) cycle-ergometry exercise.

Methods

Fifteen male cyclists completed a 7 × 4-min submaximal protocol and a 3-min all-out TT (TTAO). Linear relationships between power output (PO) and submaximal metabolic rate were constructed to estimate TT-specific gross efficiency (GE) and AnWC, using either a measured resting metabolic rate as a Y-intercept (7 + YLIN) or no measured Y-intercept (7-YLIN). In addition, GE of the last submaximal bout (GELAST) was used to estimate AnWC, and critical power (CP) from TTAO (CP3´AO) was used to estimate mechanical work above CP (W’, i.e., “AnWC”).

Results

Average PO during TTAO was 5.43 ± 0.30 and CP was 4.48 ± 0.23 W∙kg−1. The TT-associated GE values were ~ 22.0% for both 7 + YLIN and 7-YLIN and ~ 21.1% for GELAST (both P < 0.001). The AnWC were 269 ± 60, 272 ± 55, 299 ± 61, and 196 ± 52 J∙kg−1 for the 7 + YLIN, 7-YLIN, GELAST, and CP3´AO models, respectively (7 + YLIN and 7-YLIN versus GELAST, both P < 0.001; 7 + YLIN, 7-YLIN, and GELAST versus CP3´AO, all P < 0.01). For the three pair-wise comparisons between 7 + YLIN, 7-YLIN, and GELAST, typical errors in AnWC values ranged from 7 to 11 J∙kg−1, whereas 7 + YLIN, 7-YLIN, and GELAST versus CP3´AO revealed typical errors of 55–59 J∙kg−1.

Conclusion

These findings demonstrate a substantial disagreement in AnWC between CP3´AO and the other models. The 7 + YLIN and 7-YLIN generated 10% lower AnWC values than the GELAST model, whereas 7 + YLIN and 7-YLIN generated similar values of AnWC.

Physiology and Performance Prospects of a Women's Sub-4-Minute Mile

When will women run a sub-4-minute mile? The answer seems to be a distant future given how women's progress has plateaued in the mile, or its better studied metric placeholder, the 1500 m. When commonly accepted energetics principles of running, along with useful field validation equations of the same, are applied to probe the physiology underpinning the 10 all-time best women's mile performances, insights gained may help explain the present 12.34-second shortfall. Insights also afford estimates of how realistic improvements in the metabolic cost of running could shrink the difference and bring the women's world record closer to the fabled 4-minute mark. As with men in the early 1950s, this might stir greater interest, excitement, participation, and depth in the women's mile, the present absence of which likely contributes to more pessimistic mathematical modeling forecasts. The purpose of this invited commentary is to provide a succinct, theoretical, but intuitive explanation for how women might get closer to their own watershed moment in the mile.

Runners Employ Different Strategies to Cope With Increased Speeds Based on Their Initial Strike Patterns

In this paper we examined how runners with different initial foot strike pattern (FSP) develop their pattern over increasing speeds. The foot strike index (FSI) of 47 runners [66% initially rearfoot strikers (RFS)] was measured in six speeds (2.5–5.0 ms⁻¹), with the hypotheses that the FSI would increase (i.e., move toward the fore of the foot) in RFS strikers, but remain similar in mid- or forefoot strikers (MFS) runners. The majority of runners (77%) maintained their original FSP by increasing speed. However, we detected a significant (16.8%) decrease in the FSI in the MFS group as a function of running speed, showing changes in the running strategy, despite the absence of a shift from one FSP to another. Further, while both groups showed a decrease in contact times, we found a group by speed interaction (p < 0.001) and specifically that this decrease was lower in the MFS group with increasing running speeds. This could have implications in the metabolic energy consumption for MFS-runners, typically measured at low speeds for the assessment of running economy.