Effect of gradient on cycling gross efficiency and technique

Everesting: cycling the elevation of the tallest mountain on Earth

Purpose

With few cycling races on the calendar in 2020 due to COVID-19, Everesting became a popular challenge: you select one hill and cycle up and down it until you reach the accumulated elevation of Mt. Everest (8,848 m or 29,029ft). With an almost infinite number of different hills across the world, the question arises what the optimal hill for Everesting would be. Here, we address the biomechanics and energetics of up- and downhill cycling to determine the characteristics of this optimal hill.

Methods

During uphill cycling, the mechanical power output equals the power necessary to overcome air resistance, rolling resistance, and work against gravity, and for a fast Everesting time, one should maximize this latter term. To determine the optimal section length (i.e., number of repetitions), we applied the critical power concept and assumed that the U-turn associated with an additional repetition comes with a 6 s time penalty.

Results

To use most mechanical power to overcoming gravity, slopes of at least 12% are most suitable, especially since gross efficiency seems only minimally diminished on steeper slopes. Next, we found 24 repetitions to be optimal, yet this number slightly depends on the assumptions made. Finally, we discuss other factors (fueling, altitude, fatigue) not incorporated in the model but also affecting Everesting performances.

Conclusion

For a fast Everesting time, our model suggests to select a hill climb which preferably starts at (or close to) sea level, with a slope of 12–20% and length of 2–3 km.

Effects of Flat and Uphill Cycling on the Power-duration Relationship

The purpose of this study was to investigate the effects of flat and uphill cycling on critical power and the work available above critical power. Thirteen well-trained endurance athletes performed three prediction trials of 10-, 4- and 1-min in both flat (0.6%) and uphill (9.8%) cycling conditions on two separate days. Critical power and the work available above critical power were estimated using various mathematical models. The best individual fit was used for further statistical analyses. Paired t-tests and Bland-Altman plots with 95% limits of agreement were applied to compare power output and parameter estimates between cycling conditions. Power output during the 10- and 4-min prediction trial and power output at critical power were not significantly affected by test conditions (all at p>0.05), but the limits of agreement between flat and uphill cycling power output and critical power estimates are too large to consider both conditions as equivalent. However, power output during the 1-min prediction trial and the work available above critical power were significantly higher during uphill compared to flat cycling (p<0.05). The results of this investigation indicate that gradient affects cycling time-trial performance, power output at critical power, and the amount of work available above critical power.

Effect of cycling specialization on effort and physiological responses to uphill and flat cycling at similar intensity

Power output is considered one of the best tools to control external loads in cycling, but the relationship between a target power output and the physiological responses may suffer from the effects of road gradient, which is also affected by cyclist specialization. The objective was to determine the effects of cyclist specialization on effort perception and physiological response (heart rate and lactate concentration) while sustaining efforts at similar power output but riding on two different road gradients. Nineteen male competitive road cyclists performed two randomized trials of 10 min at 0% (velodrome) and 10 min at 6% road gradient (field uphill), at an intensity of 10% ± 3% below the individual's functional threshold power. Cadence was kept between 75 and 80 rpm in both trials and posture remained unchanged during the tests. Heart rate, speed, cadence, power output, blood lactate, and rate of perceived effort were measured for each trial. K-means cluster analyses differentiate uphill (n = 10) and flat specialists (n = 9) according to lactate responses. Flat specialists presented lower heart rate (p < 0.001 and ES = 0.2), perceived exertion (p < 0.01 and ES = 0.7), and blood lactate concentration (p < 0.001 and ES = 0.7) riding on the flat than uphill. Uphill specialists presented lower perceived exertion (p < 0.01 and ES = 0.8) and blood lactate concentration (p < 0.01 and ES = 0.5) riding uphill than on the flat. In conclusion, the combination of cyclist specialization and road gradient affects physiological and effort perception parameters in response to a similar power output demand. These factors deserve attention in training schedules and monitoring performance using power output data.

Phase space methods for non-linear analysis of pedalling forces in cycling

Introduction

From the perspective of dynamic systems theory, stability and variability of biological signals are both understood as a functional adaptation to variable environmental conditions. In the present study, we examined whether this theoretical perspective is applicable to the pedalling movement in cycling. Non-linear measures were applied to analyse pedalling forces with varying levels of subjective load.

Materials and methods

Ten subjects completed a 13-sector virtual terrain profile of 15 km total length on a roller trainer with varying degrees of virtual terrain inclination (resistance). The test was repeated two times with different instructions on how to alter the bikes gearing. During the experiment, pedalling force and heart rate were measured. Force-time curves were sequenced into single cycles, linearly interpolated in the time domain, and z-score normalised. The established time series was transferred into a two-dimensional phase space with limit cycle properties given the applied 25% phase shift. Different representations of the phase space attractor were calculated within each sector and used as non-linear measures assessing pedalling forces.

Results and discussion

A contrast analysis showed that changes in pedalling load were strongly associated to changes in non-linear phase space attractor variables. For the subjects investigated in this study, this association was stronger than that between heart rate and resistance level. The results indicate systematic changes of the pedalling movement as an adaptive response to an externally determined increase in workload. Future research may utilise the findings from this study to investigate possible relationships between subjective measures of exhaustion, comfort, and discomfort with biomechanic characteristics of the pedalling movement and to evaluate connections with dynamic stability measures.

Determining optimal cadence for an individual road cyclist from field data

The cadence that maximises power output developed at the crank by an individual cyclist is conventionally determined using a laboratory test. The purpose of this study was two-fold: (i) to show that such a cadence, which we call the optimal cadence, can be determined using power output, heart-rate, and cadence measured in the field and (ii) to describe methodology to do so. For an individual cyclist's sessions, power output is related to cadence and the elicited heart-rate using a non-linear regression model. Optimal cadences are found for two riders (83 and 70 revolutions per minute, respectively); these cadences are similar to the riders’ preferred cadences (82–92 rpm and 65–75 rpm). Power output reduces by approximately 6% for cadences 20 rpm above or below optimum. Our methodology can be used by a rider to determine an optimal cadence without laboratory testing intervention: the rider will need to collect power output, heart-rate, and cadence measurements from training and racing sessions over an extended period (>6 months); ride at a range of cadences within those sessions; and calculate his/her optimal cadence using the methodology described or a software tool that implements it.